Repository: VIDA-NYU/alpha-automl (language: python)

Issue:
No such file or directory: 'tmp/nn_models'
I tried to run a pipeline search (from [tabular_classification.ipynb](https://github.com/VIDA-NYU/alpha-automl/blob/devel/examples/tabular_classification.ipynb)) and it failed with the error:
```
Process Process-1:
Traceback (most recent call last):
  File "/opt/conda/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/opt/conda/lib/python3.9/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/opt/conda/lib/python3.9/site-packages/alpha_automl/pipeline_synthesis/setup_search.py", line 112, in search_pipelines
    c.learn()
  File "/opt/conda/lib/python3.9/site-packages/alpha_automl/pipeline_search/Coach.py", line 101, in learn
    self.nnet.save_checkpoint(folder=self.args.get('checkpoint'), filename='temp.pth.tar')
  File "/opt/conda/lib/python3.9/site-packages/alpha_automl/pipeline_search/pipeline/NNet.py", line 145, in save_checkpoint
    os.mkdir(folder)
FileNotFoundError: [Errno 2] No such file or directory: 'tmp/nn_models'
```
To solve it, I had to create the folder `tmp` manually in the home directory of the JupyterLab installation. Does alpha-automl create this folder automatically?


Discussion / hints:
In addition, the cell running the method `fit()` does not return when the error happens. It seems to stay blocked running indefinitely until the user interrupts the cell.
Thanks @aecio! Do you mean that it shows that error and gets 'frozen'?
Per discussion, [tempfile](https://docs.python.org/3/library/tempfile.html#module-tempfile) should be used on this.
In the case that the parameter `output_path` is provided (as in the linked notebook), I think we should just automatically create the directory if it does not exist.
Yes, `output_path` will be an optional param (`output_path=None`), if provided we will use/create it, otherwise use `tempfile`.

Fix patch:
diff --git a/alpha_automl/automl_api.py b/alpha_automl/automl_api.py
index b5047150..565bc94d 100644
--- a/alpha_automl/automl_api.py
+++ b/alpha_automl/automl_api.py
@@ -1,4 +1,3 @@
-import os
 import sys
 import logging
 import datetime
@@ -7,7 +6,8 @@
 from sklearn.preprocessing import LabelEncoder
 from alpha_automl.automl_manager import AutoMLManager
 from alpha_automl.scorer import make_scorer, make_splitter, make_str_metric, get_sign_sorting
-from alpha_automl.utils import make_d3m_pipelines, hide_logs, get_start_method, check_input_for_multiprocessing, SemiSupervisedSplitter, SemiSupervisedLabelEncoder
+from alpha_automl.utils import make_d3m_pipelines, hide_logs, get_start_method, check_input_for_multiprocessing, \
+    setup_output_folder, SemiSupervisedSplitter, SemiSupervisedLabelEncoder
 from alpha_automl.visualization import plot_comparison_pipelines
 
 
@@ -20,13 +20,12 @@
 
 class BaseAutoML():
 
-    def __init__(self, output_folder, time_bound=15, metric=None, split_strategy='holdout', time_bound_run=5, task=None,
-                 score_sorting='auto', metric_kwargs=None, split_strategy_kwargs=None, start_mode='auto',
-                 verbose=logging.INFO):
+    def __init__(self, time_bound=15, metric=None, split_strategy='holdout', time_bound_run=5, task=None,
+                 score_sorting='auto', metric_kwargs=None, split_strategy_kwargs=None,  output_folder=None,
+                 start_mode='auto', verbose=logging.INFO):
         """
         Create/instantiate an BaseAutoML object.
 
-        :param output_folder: Path to the output directory
         :param time_bound: Limit time in minutes to perform the search
         :param metric: A str (see in the documentation the list of available metrics) or a callable object/function
         :param split_strategy: Method to score the pipeline: `holdout`, `cross_validation` or an instance of
@@ -37,11 +36,11 @@ def __init__(self, output_folder, time_bound=15, metric=None, split_strategy='ho
             `auto` is used for the built-in metrics. For the user-defined metrics, this param must be passed.
         :param metric_kwargs: Additional arguments for metric
         :param split_strategy_kwargs: Additional arguments for splitting_strategy.
+        :param output_folder: Path to the output directory. If it is None, create a temp folder automatically.
         :param start_mode: The mode to start the multiprocessing library. It could be `auto`, `fork` or `spawn`.
         :param verbose: Whether or not to show additional logs
         """
 
-        self.output_folder = output_folder
         self.time_bound = time_bound
         self.time_bound_run = time_bound_run
         self.metric = make_str_metric(metric)
@@ -51,16 +50,16 @@ def __init__(self, output_folder, time_bound=15, metric=None, split_strategy='ho
         self.scorer = make_scorer(metric, metric_kwargs)
         self.score_sorting = score_sorting
         self.splitter = make_splitter(split_strategy, split_strategy_kwargs)
+        self.output_folder = setup_output_folder(output_folder)
         self.pipelines = {}
         self.new_primitives = {}
         self.X = None
         self.y = None
         self.leaderboard = None
-        self.automl_manager = AutoMLManager(output_folder, time_bound, time_bound_run, task, verbose)
+        self.automl_manager = AutoMLManager(self.output_folder, time_bound, time_bound_run, task, verbose)
 
         hide_logs(verbose)
-
-        os.makedirs(output_folder, exist_ok=True)
+        
         self._start_method = get_start_method(start_mode)
         set_start_method(self._start_method, force=True)
         check_input_for_multiprocessing(self._start_method, self.scorer._score_func, 'metric')
@@ -289,13 +288,12 @@ def _score(self, X, y, pipeline_id):
 
 class AutoMLClassifier(BaseAutoML):
 
-    def __init__(self, output_folder, time_bound=15, metric='accuracy_score', split_strategy='holdout',
-                 time_bound_run=5, score_sorting='auto', metric_kwargs=None, split_strategy_kwargs=None,
+    def __init__(self, time_bound=15, metric='accuracy_score', split_strategy='holdout', time_bound_run=5,
+                 score_sorting='auto', metric_kwargs=None, split_strategy_kwargs=None, output_folder=None,
                  start_mode='auto', verbose=logging.INFO):
         """
         Create/instantiate an AutoMLClassifier object.
 
-        :param output_folder: Path to the output directory.
         :param time_bound: Limit time in minutes to perform the search.
         :param metric: A str (see in the documentation the list of available metrics) or a callable object/function.
         :param split_strategy: Method to score the pipeline: `holdout`, `cross_validation` or an instance of
@@ -305,14 +303,15 @@ def __init__(self, output_folder, time_bound=15, metric='accuracy_score', split_
             `auto` is used for the built-in metrics. For the user-defined metrics, this param must be passed.
         :param metric_kwargs: Additional arguments for metric.
         :param split_strategy_kwargs: Additional arguments for splitting_strategy.
+        :param output_folder: Path to the output directory. If it is None, create a temp folder automatically.
         :param start_mode: The mode to start the multiprocessing library. It could be `auto`, `fork` or `spawn`.
         :param verbose: Whether or not to show additional logs.
         """
 
         self.label_enconder = LabelEncoder()
         task = 'CLASSIFICATION'
-        super().__init__(output_folder, time_bound, metric, split_strategy, time_bound_run, task, score_sorting,
-                         metric_kwargs, split_strategy_kwargs, start_mode, verbose)
+        super().__init__(time_bound, metric, split_strategy, time_bound_run, task, score_sorting, metric_kwargs,
+                         split_strategy_kwargs, output_folder, start_mode, verbose)
 
     def fit(self, X, y):
         y = self.label_enconder.fit_transform(y)
@@ -344,13 +343,12 @@ def score_pipeline(self, X, y, pipeline_id):
 
 class AutoMLRegressor(BaseAutoML):
 
-    def __init__(self, output_folder, time_bound=15, metric='mean_absolute_error', split_strategy='holdout',
-                 time_bound_run=5, score_sorting='auto', metric_kwargs=None, split_strategy_kwargs=None,
+    def __init__(self, time_bound=15, metric='mean_absolute_error', split_strategy='holdout', time_bound_run=5,
+                 score_sorting='auto', metric_kwargs=None, split_strategy_kwargs=None, output_folder=None,
                  start_mode='auto', verbose=logging.INFO):
         """
         Create/instantiate an AutoMLRegressor object.
 
-        :param output_folder: Path to the output directory.
         :param time_bound: Limit time in minutes to perform the search.
         :param metric: A str (see in the documentation the list of available metrics) or a callable object/function.
         :param split_strategy: Method to score the pipeline: `holdout`, `cross_validation` or an instance of
@@ -360,23 +358,23 @@ def __init__(self, output_folder, time_bound=15, metric='mean_absolute_error', s
             `auto` is used for the built-in metrics. For the user-defined metrics, this param must be passed.
         :param metric_kwargs: Additional arguments for metric.
         :param split_strategy_kwargs: Additional arguments for splitting_strategy.
+        :param output_folder: Path to the output directory. If it is None, create a temp folder automatically.
         :param start_mode: The mode to start the multiprocessing library. It could be `auto`, `fork` or `spawn`.
         :param verbose: Whether or not to show additional logs.
         """
 
         task = 'REGRESSION'
-        super().__init__(output_folder, time_bound, metric, split_strategy, time_bound_run, task, score_sorting,
-                         metric_kwargs, split_strategy_kwargs, start_mode, verbose)
+        super().__init__(time_bound, metric, split_strategy, time_bound_run, task, score_sorting, metric_kwargs,
+                         split_strategy_kwargs, output_folder, start_mode, verbose)
 
         
 class AutoMLTimeSeries(BaseAutoML):
-    def __init__(self, output_folder, time_bound=15, metric='mean_squared_error', split_strategy='timeseries',
-                 time_bound_run=5, score_sorting='auto', metric_kwargs=None, split_strategy_kwargs=None,
-                 verbose=logging.INFO, date_column=None, target_column=None):
+    def __init__(self, time_bound=15, metric='mean_squared_error', split_strategy='timeseries', time_bound_run=5,
+                 score_sorting='auto', metric_kwargs=None, split_strategy_kwargs=None, output_folder=None,
+                 start_mode='auto', verbose=logging.INFO, date_column=None, target_column=None):
         """
         Create/instantiate an AutoMLTimeSeries object.
 
-        :param output_folder: Path to the output directory.
         :param time_bound: Limit time in minutes to perform the search.
         :param metric: A str (see in the documentation the list of available metrics) or a callable object/function.
         :param split_strategy: Method to score the pipeline: `holdout`, `cross_validation` or an instance of
@@ -386,6 +384,7 @@ def __init__(self, output_folder, time_bound=15, metric='mean_squared_error', sp
             `auto` is used for the built-in metrics. For the user-defined metrics, this param must be passed.
         :param metric_kwargs: Additional arguments for metric.
         :param split_strategy_kwargs: Additional arguments for TimeSeriesSplit, E.g. n_splits and test_size(int).
+        :param output_folder: Path to the output directory. If it is None, create a temp folder automatically.
         :param start_mode: The mode to start the multiprocessing library. It could be `auto`, `fork` or `spawn`.
         :param verbose: Whether or not to show additional logs.
         """
@@ -393,8 +392,8 @@ def __init__(self, output_folder, time_bound=15, metric='mean_squared_error', sp
         task = 'TIME_SERIES_FORECAST'
         self.date_column = date_column
         self.target_column = target_column
-        super().__init__(output_folder, time_bound, metric, split_strategy, time_bound_run, task, score_sorting,
-                         metric_kwargs, split_strategy_kwargs, verbose)
+        super().__init__(time_bound, metric, split_strategy, time_bound_run, task, score_sorting, metric_kwargs,
+                         split_strategy_kwargs, output_folder, start_mode, verbose)
         
     def _column_parser(self, X):
         cols = list(X.columns.values)
@@ -411,13 +410,12 @@ def fit(self, X, y=None):
 
 class AutoMLSemiSupervisedClassifier(BaseAutoML):
 
-    def __init__(self, output_folder, time_bound=15, metric='f1_score', split_strategy='holdout',
-                 time_bound_run=5, score_sorting='auto', metric_kwargs={'average': 'micro'}, split_strategy_kwargs=None,
-                 start_mode='auto', verbose=logging.INFO):
+    def __init__(self, time_bound=15, metric='f1_score', split_strategy='holdout', time_bound_run=5,
+                 score_sorting='auto', metric_kwargs={'average': 'micro'}, split_strategy_kwargs=None,
+                 output_folder=None, start_mode='auto', verbose=logging.INFO):
         """
         Create/instantiate an AutoMLSemiSupervisedClassifier object.
         
-        :param output_folder: Path to the output directory.
         :param time_bound: Limit time in minutes to perform the search.
         :param metric: A str (see in the documentation the list of available metrics) or a callable object/function.
         :param split_strategy: Method to score the pipeline: `holdout`, `cross_validation` or an instance of
@@ -428,13 +426,14 @@ def __init__(self, output_folder, time_bound=15, metric='f1_score', split_strate
         :param metric_kwargs: Additional arguments for metric.
         :param split_strategy_kwargs: Additional arguments for splitting_strategy. In SemiSupervised case, `n_splits`
             and `test_size`(test proportion from 0 to 1) can be pass to the splitter.
+        :param output_folder: Path to the output directory. If it is None, create a temp folder automatically.
         :param start_mode: The mode to start the multiprocessing library. It could be `auto`, `fork` or `spawn`.
         :param verbose: Whether or not to show additional logs.
         """
         self.label_enconder = SemiSupervisedLabelEncoder()
         task = 'SEMISUPERVISED'
-        super().__init__(output_folder, time_bound, metric, split_strategy, time_bound_run, task, score_sorting,
-                         metric_kwargs, split_strategy_kwargs, start_mode, verbose)
+        super().__init__(time_bound, metric, split_strategy, time_bound_run, task, score_sorting, metric_kwargs,
+                         split_strategy_kwargs, output_folder, start_mode, verbose)
 
         if split_strategy_kwargs is None:
             split_strategy_kwargs = {'test_size': 0.2}
diff --git a/alpha_automl/pipeline_search/pipeline/NNet.py b/alpha_automl/pipeline_search/pipeline/NNet.py
index aac5348b..f364f4ab 100644
--- a/alpha_automl/pipeline_search/pipeline/NNet.py
+++ b/alpha_automl/pipeline_search/pipeline/NNet.py
@@ -122,7 +122,7 @@ def loss_v(self, targets, outputs):
     def save_checkpoint(self, folder='checkpoint', filename='checkpoint.pth.tar'):
         filepath = os.path.join(folder, filename)
         if not os.path.exists(folder):
-            logger.warning("Checkpoint Directory does not exist! Making directory {}".format(folder))
+            logger.debug("Checkpoint Directory does not exist! Making directory {}".format(folder))
             os.mkdir(folder)
         else:
             logger.debug("Checkpoint Directory exists! ")
diff --git a/alpha_automl/utils.py b/alpha_automl/utils.py
index a3689fd9..63290a01 100644
--- a/alpha_automl/utils.py
+++ b/alpha_automl/utils.py
@@ -1,10 +1,10 @@
-import sys
+import os
 import importlib
 import inspect
 import logging
 import platform
 import warnings
-
+import tempfile
 import numpy as np
 import pandas as pd
 import torch
@@ -211,6 +211,16 @@ def hide_logs(level):
     logging.getLogger('alpha_automl').setLevel(level)
 
 
+def setup_output_folder(output_folder):
+    if output_folder is None:
+        output_folder = tempfile.mkdtemp(prefix="alpha_automl", suffix="_log")
+        logger.debug(f'Created temporary directory: {output_folder}')
+    else:
+        os.makedirs(output_folder, exist_ok=True)
+
+    return output_folder
+
+
 def get_start_method(suggested_method):
     device = 'cuda' if torch.cuda.is_available() else 'cpu'
     operating_system = platform.system()
diff --git a/docs/source/getting-started.ipynb b/docs/source/getting-started.ipynb
index 24a12ddf..72931686 100644
--- a/docs/source/getting-started.ipynb
+++ b/docs/source/getting-started.ipynb
@@ -69,7 +69,7 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "output_path = 'tmp/'\n",
+    "\n",
     "train_dataset = pd.read_csv('../../examples/datasets/299_libras_move/train_data.csv')\n",
     "test_dataset = pd.read_csv('../../examples/datasets/299_libras_move/test_data.csv')"
    ]
@@ -924,7 +924,7 @@
     }
    ],
    "source": [
-    "automl = AutoMLClassifier(output_path, time_bound=1)\n",
+    "automl = AutoMLClassifier(time_bound=1)\n",
     "automl.fit(X_train, y_train)"
    ]
   },
diff --git a/examples/adding_fasttext_primitives.ipynb b/examples/adding_fasttext_primitives.ipynb
index a3d41dc7..d73545f1 100644
--- a/examples/adding_fasttext_primitives.ipynb
+++ b/examples/adding_fasttext_primitives.ipynb
@@ -44,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_path = 'tmp/'\n",
+    "\n",
     "train_dataset = pd.read_csv('datasets/sentiment/train_data.csv')\n",
     "test_dataset = pd.read_csv('datasets/sentiment/test_data.csv')"
    ]
@@ -338,7 +338,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "automl = AutoMLClassifier(output_path, time_bound=10)"
+    "automl = AutoMLClassifier(time_bound=10)"
    ]
   },
   {
diff --git a/examples/adding_huggingface_image_primitives.ipynb b/examples/adding_huggingface_image_primitives.ipynb
index 42d4b7d3..36fc9382 100644
--- a/examples/adding_huggingface_image_primitives.ipynb
+++ b/examples/adding_huggingface_image_primitives.ipynb
@@ -130,7 +130,6 @@
     "\n",
     "from alpha_automl import AutoMLClassifier\n",
     "\n",
-    "output_path = 'tmp/'\n",
     "media_path = os.path.join(os.getcwd(), 'datasets/selfie/')\n",
     "dataset = pd.read_csv('datasets/selfie/learningData.csv').sample(1000)\n",
     "dataset[\"image\"] = dataset[\"image\"].apply(lambda x: os.path.join(media_path, x))\n",
@@ -182,8 +181,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_path = 'tmp/'\n",
-    "automl = AutoMLClassifier(output_path, time_bound=20)"
+    "\n",
+    "automl = AutoMLClassifier(time_bound=20)"
    ]
   },
   {
diff --git a/examples/adding_huggingface_text_primitives.ipynb b/examples/adding_huggingface_text_primitives.ipynb
index 2af52a9d..06082e7d 100644
--- a/examples/adding_huggingface_text_primitives.ipynb
+++ b/examples/adding_huggingface_text_primitives.ipynb
@@ -44,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_path = 'tmp/'\n",
+    "\n",
     "train_dataset = pd.read_csv('datasets/sentiment/train_data.csv')\n",
     "test_dataset = pd.read_csv('datasets/sentiment/test_data.csv')"
    ]
@@ -338,7 +338,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "automl = AutoMLClassifier(output_path, time_bound=60)"
+    "automl = AutoMLClassifier(time_bound=60)"
    ]
   },
   {
diff --git a/examples/adding_sklearnlike_primitives.ipynb b/examples/adding_sklearnlike_primitives.ipynb
index 338c4591..7827b711 100644
--- a/examples/adding_sklearnlike_primitives.ipynb
+++ b/examples/adding_sklearnlike_primitives.ipynb
@@ -44,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_path = 'tmp/'\n",
+    "\n",
     "train_dataset = pd.read_csv('datasets/299_libras_move/train_data.csv')\n",
     "test_dataset = pd.read_csv('datasets/299_libras_move/test_data.csv')"
    ]
@@ -562,7 +562,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "automl = AutoMLClassifier(output_path, time_bound=1)"
+    "automl = AutoMLClassifier(time_bound=1)"
    ]
   },
   {
diff --git a/examples/adding_your_custom_primitives.ipynb b/examples/adding_your_custom_primitives.ipynb
index cd76bfe9..abeb55c2 100644
--- a/examples/adding_your_custom_primitives.ipynb
+++ b/examples/adding_your_custom_primitives.ipynb
@@ -44,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_path = 'tmp/'\n",
+    "\n",
     "train_dataset = pd.read_csv('datasets/299_libras_move/train_data.csv')\n",
     "test_dataset = pd.read_csv('datasets/299_libras_move/test_data.csv')"
    ]
@@ -555,7 +555,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "automl = AutoMLClassifier(output_path, time_bound=1)"
+    "automl = AutoMLClassifier(time_bound=1)"
    ]
   },
   {
diff --git a/examples/semi_supervised_classification.ipynb b/examples/semi_supervised_classification.ipynb
index 64d6f277..3ba5e8f7 100644
--- a/examples/semi_supervised_classification.ipynb
+++ b/examples/semi_supervised_classification.ipynb
@@ -40,7 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_path = 'tmp/'\n",
+    "\n",
     "train_dataset = pd.read_csv('datasets/LL0_1053_jm1/train_data.csv')\n",
     "test_dataset = pd.read_csv('datasets/LL0_1053_jm1/test_data.csv')"
    ]
@@ -655,7 +655,7 @@
     }
    ],
    "source": [
-    "automl = AutoMLSemiSupervisedClassifier(output_path, time_bound=10, start_mode='spawn')\n",
+    "automl = AutoMLSemiSupervisedClassifier(time_bound=10, start_mode='spawn')\n",
     "automl.fit(X_train, y_train)"
    ]
   },
diff --git a/examples/tabular_classification.ipynb b/examples/tabular_classification.ipynb
index b947cabc..608db993 100644
--- a/examples/tabular_classification.ipynb
+++ b/examples/tabular_classification.ipynb
@@ -44,7 +44,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_path = 'tmp/'\n",
     "train_dataset = pd.read_csv('datasets/299_libras_move/train_data.csv')\n",
     "test_dataset = pd.read_csv('datasets/299_libras_move/test_data.csv')"
    ]
@@ -560,6 +559,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "INFO:alpha_automl.automl_api:created temporary directory: /tmp/alpha_automlrev5sz01_log\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:01, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.125\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:01, scoring...\n",
@@ -576,7 +576,7 @@
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.7777777777777778\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:02, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.5416666666666666\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:03, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:02, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.6805555555555556\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:09, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.5555555555555556\n",
@@ -598,11 +598,11 @@
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.25\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:09, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.19444444444444445\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:10, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:09, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.7361111111111112\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:10, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:09, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.027777777777777776\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:10, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:09, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.5416666666666666\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:10, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.5972222222222222\n",
@@ -618,19 +618,19 @@
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.18055555555555555\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:10, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.19444444444444445\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:11, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.20833333333333334\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:11, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.18055555555555555\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:11, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.75\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:11, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.3194444444444444\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:11, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.20833333333333334\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:11, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.19444444444444445\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:11, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.125\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.7916666666666666\n",
@@ -644,13 +644,13 @@
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.4583333333333333\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.4444444444444444\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:13, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.4444444444444444\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:13, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.125\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:13, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.3472222222222222\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:12, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:13, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.19444444444444445\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:13, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.4166666666666667\n",
@@ -668,17 +668,17 @@
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.4166666666666667\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:13, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.4305555555555556\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:13, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:14, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.5\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:13, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:14, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.125\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:13, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:14, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.25\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:14, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.5555555555555556\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:14, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.19444444444444445\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:15, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:16, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.375\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:16, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.3194444444444444\n",
@@ -732,7 +732,7 @@
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.2222222222222222\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:20, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.4444444444444444\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:21, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:20, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.4444444444444444\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:21, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.18055555555555555\n",
@@ -782,11 +782,11 @@
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.1388888888888889\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:37, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.5138888888888888\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:38, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:37, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.7361111111111112\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:38, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:37, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.5694444444444444\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:38, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:37, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.25\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:38, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.4305555555555556\n",
@@ -806,11 +806,11 @@
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.3611111111111111\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:38, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.125\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:39, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:38, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.19444444444444445\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:39, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:38, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.6388888888888888\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:39, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:38, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.09722222222222222\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:39, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.7361111111111112\n",
@@ -820,13 +820,13 @@
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.7638888888888888\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:39, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.75\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:40, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:39, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.6388888888888888\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:40, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.6111111111111112\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:41, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:40, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.6111111111111112\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:41, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:40, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.20833333333333334\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:41, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.19444444444444445\n",
@@ -842,9 +842,9 @@
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.6388888888888888\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:41, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.5277777777777778\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:42, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:41, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.2361111111111111\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:42, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:41, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.7638888888888888\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:42, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.6111111111111112\n",
@@ -852,52 +852,52 @@
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.1388888888888889\n",
       "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:42, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.7916666666666666\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:43, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:42, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.4166666666666667\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:43, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:42, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.6944444444444444\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:43, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:42, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.06944444444444445\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:43, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:42, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.09722222222222222\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:43, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:42, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.6111111111111112\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:43, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:42, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.3472222222222222\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:48, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:47, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.3472222222222222\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:48, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:47, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.3472222222222222\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:50, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:49, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.25\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:50, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:49, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.1527777777777778\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:53, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:51, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.19444444444444445\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:53, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:52, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.18055555555555555\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:53, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:52, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.09722222222222222\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:53, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:52, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.1527777777777778\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:53, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:52, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.25\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:59, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:57, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.1388888888888889\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:59, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:58, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.06944444444444445\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:59, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:58, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.18055555555555555\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:01:00, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:00:58, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.19444444444444445\n",
-      "INFO:alpha_automl.automl_api:Found pipeline, time=0:01:03, scoring...\n",
+      "INFO:alpha_automl.automl_api:Found pipeline, time=0:01:01, scoring...\n",
       "INFO:alpha_automl.automl_api:Scored pipeline, score=0.7222222222222222\n",
       "INFO:alpha_automl.automl_api:Found 166 pipelines\n"
      ]
     }
    ],
    "source": [
-    "automl = AutoMLClassifier(output_path, time_bound=10)\n",
+    "automl = AutoMLClassifier(time_bound=1)\n",
     "automl.fit(X_train, y_train)"
    ]
   },
@@ -917,7 +917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "scrolled": true
    },
@@ -927,853 +927,853 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_9ea44\">\n",
+       "<table id=\"T_8ffa7\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th id=\"T_9ea44_level0_col0\" class=\"col_heading level0 col0\" >ranking</th>\n",
-       "      <th id=\"T_9ea44_level0_col1\" class=\"col_heading level0 col1\" >pipeline</th>\n",
-       "      <th id=\"T_9ea44_level0_col2\" class=\"col_heading level0 col2\" >accuracy_score</th>\n",
+       "      <th id=\"T_8ffa7_level0_col0\" class=\"col_heading level0 col0\" >ranking</th>\n",
+       "      <th id=\"T_8ffa7_level0_col1\" class=\"col_heading level0 col1\" >pipeline</th>\n",
+       "      <th id=\"T_8ffa7_level0_col2\" class=\"col_heading level0 col2\" >accuracy_score</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row0_col0\" class=\"data row0 col0\" >1</td>\n",
-       "      <td id=\"T_9ea44_row0_col1\" class=\"data row0 col1\" >StandardScaler, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row0_col2\" class=\"data row0 col2\" >0.792</td>\n",
+       "      <td id=\"T_8ffa7_row0_col0\" class=\"data row0 col0\" >1</td>\n",
+       "      <td id=\"T_8ffa7_row0_col1\" class=\"data row0 col1\" >StandardScaler, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row0_col2\" class=\"data row0 col2\" >0.792</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row1_col0\" class=\"data row1 col0\" >2</td>\n",
-       "      <td id=\"T_9ea44_row1_col1\" class=\"data row1 col1\" >StandardScaler, LogisticRegression</td>\n",
-       "      <td id=\"T_9ea44_row1_col2\" class=\"data row1 col2\" >0.792</td>\n",
+       "      <td id=\"T_8ffa7_row1_col0\" class=\"data row1 col0\" >2</td>\n",
+       "      <td id=\"T_8ffa7_row1_col1\" class=\"data row1 col1\" >StandardScaler, LogisticRegression</td>\n",
+       "      <td id=\"T_8ffa7_row1_col2\" class=\"data row1 col2\" >0.792</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row2_col0\" class=\"data row2 col0\" >3</td>\n",
-       "      <td id=\"T_9ea44_row2_col1\" class=\"data row2 col1\" >MaxAbsScaler, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row2_col2\" class=\"data row2 col2\" >0.778</td>\n",
+       "      <td id=\"T_8ffa7_row2_col0\" class=\"data row2 col0\" >3</td>\n",
+       "      <td id=\"T_8ffa7_row2_col1\" class=\"data row2 col1\" >MaxAbsScaler, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row2_col2\" class=\"data row2 col2\" >0.778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row3_col0\" class=\"data row3 col0\" >4</td>\n",
-       "      <td id=\"T_9ea44_row3_col1\" class=\"data row3 col1\" >MaxAbsScaler, SVC</td>\n",
-       "      <td id=\"T_9ea44_row3_col2\" class=\"data row3 col2\" >0.764</td>\n",
+       "      <td id=\"T_8ffa7_row3_col0\" class=\"data row3 col0\" >4</td>\n",
+       "      <td id=\"T_8ffa7_row3_col1\" class=\"data row3 col1\" >MaxAbsScaler, SVC</td>\n",
+       "      <td id=\"T_8ffa7_row3_col2\" class=\"data row3 col2\" >0.764</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row4_col0\" class=\"data row4 col0\" >5</td>\n",
-       "      <td id=\"T_9ea44_row4_col1\" class=\"data row4 col1\" >SVC</td>\n",
-       "      <td id=\"T_9ea44_row4_col2\" class=\"data row4 col2\" >0.764</td>\n",
+       "      <td id=\"T_8ffa7_row4_col0\" class=\"data row4 col0\" >5</td>\n",
+       "      <td id=\"T_8ffa7_row4_col1\" class=\"data row4 col1\" >SVC</td>\n",
+       "      <td id=\"T_8ffa7_row4_col2\" class=\"data row4 col2\" >0.764</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row5_col0\" class=\"data row5 col0\" >6</td>\n",
-       "      <td id=\"T_9ea44_row5_col1\" class=\"data row5 col1\" >StandardScaler, RandomForestClassifier</td>\n",
-       "      <td id=\"T_9ea44_row5_col2\" class=\"data row5 col2\" >0.764</td>\n",
+       "      <td id=\"T_8ffa7_row5_col0\" class=\"data row5 col0\" >6</td>\n",
+       "      <td id=\"T_8ffa7_row5_col1\" class=\"data row5 col1\" >StandardScaler, RandomForestClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row5_col2\" class=\"data row5 col2\" >0.764</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row6_col0\" class=\"data row6 col0\" >7</td>\n",
-       "      <td id=\"T_9ea44_row6_col1\" class=\"data row6 col1\" >ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row6_col2\" class=\"data row6 col2\" >0.750</td>\n",
+       "      <td id=\"T_8ffa7_row6_col0\" class=\"data row6 col0\" >7</td>\n",
+       "      <td id=\"T_8ffa7_row6_col1\" class=\"data row6 col1\" >ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row6_col2\" class=\"data row6 col2\" >0.750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row7_col0\" class=\"data row7 col0\" >8</td>\n",
-       "      <td id=\"T_9ea44_row7_col1\" class=\"data row7 col1\" >RobustScaler, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row7_col2\" class=\"data row7 col2\" >0.750</td>\n",
+       "      <td id=\"T_8ffa7_row7_col0\" class=\"data row7 col0\" >8</td>\n",
+       "      <td id=\"T_8ffa7_row7_col1\" class=\"data row7 col1\" >RobustScaler, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row7_col2\" class=\"data row7 col2\" >0.750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row8_col0\" class=\"data row8 col0\" >9</td>\n",
-       "      <td id=\"T_9ea44_row8_col1\" class=\"data row8 col1\" >StandardScaler, SVC</td>\n",
-       "      <td id=\"T_9ea44_row8_col2\" class=\"data row8 col2\" >0.750</td>\n",
+       "      <td id=\"T_8ffa7_row8_col0\" class=\"data row8 col0\" >9</td>\n",
+       "      <td id=\"T_8ffa7_row8_col1\" class=\"data row8 col1\" >StandardScaler, SVC</td>\n",
+       "      <td id=\"T_8ffa7_row8_col2\" class=\"data row8 col2\" >0.750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row9_col0\" class=\"data row9 col0\" >10</td>\n",
-       "      <td id=\"T_9ea44_row9_col1\" class=\"data row9 col1\" >MaxAbsScaler, LinearSVC</td>\n",
-       "      <td id=\"T_9ea44_row9_col2\" class=\"data row9 col2\" >0.736</td>\n",
+       "      <td id=\"T_8ffa7_row9_col0\" class=\"data row9 col0\" >10</td>\n",
+       "      <td id=\"T_8ffa7_row9_col1\" class=\"data row9 col1\" >MaxAbsScaler, LinearSVC</td>\n",
+       "      <td id=\"T_8ffa7_row9_col2\" class=\"data row9 col2\" >0.736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row10_col0\" class=\"data row10 col0\" >11</td>\n",
-       "      <td id=\"T_9ea44_row10_col1\" class=\"data row10 col1\" >MaxAbsScaler, RandomForestClassifier</td>\n",
-       "      <td id=\"T_9ea44_row10_col2\" class=\"data row10 col2\" >0.736</td>\n",
+       "      <td id=\"T_8ffa7_row10_col0\" class=\"data row10 col0\" >11</td>\n",
+       "      <td id=\"T_8ffa7_row10_col1\" class=\"data row10 col1\" >MaxAbsScaler, RandomForestClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row10_col2\" class=\"data row10 col2\" >0.736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row11_col0\" class=\"data row11 col0\" >12</td>\n",
-       "      <td id=\"T_9ea44_row11_col1\" class=\"data row11 col1\" >LinearSVC</td>\n",
-       "      <td id=\"T_9ea44_row11_col2\" class=\"data row11 col2\" >0.736</td>\n",
+       "      <td id=\"T_8ffa7_row11_col0\" class=\"data row11 col0\" >12</td>\n",
+       "      <td id=\"T_8ffa7_row11_col1\" class=\"data row11 col1\" >LinearSVC</td>\n",
+       "      <td id=\"T_8ffa7_row11_col2\" class=\"data row11 col2\" >0.736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row12_col0\" class=\"data row12 col0\" >13</td>\n",
-       "      <td id=\"T_9ea44_row12_col1\" class=\"data row12 col1\" >StandardScaler, LinearSVC</td>\n",
-       "      <td id=\"T_9ea44_row12_col2\" class=\"data row12 col2\" >0.736</td>\n",
+       "      <td id=\"T_8ffa7_row12_col0\" class=\"data row12 col0\" >13</td>\n",
+       "      <td id=\"T_8ffa7_row12_col1\" class=\"data row12 col1\" >StandardScaler, LinearSVC</td>\n",
+       "      <td id=\"T_8ffa7_row12_col2\" class=\"data row12 col2\" >0.736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row13_col0\" class=\"data row13 col0\" >14</td>\n",
-       "      <td id=\"T_9ea44_row13_col1\" class=\"data row13 col1\" >RandomForestClassifier</td>\n",
-       "      <td id=\"T_9ea44_row13_col2\" class=\"data row13 col2\" >0.736</td>\n",
+       "      <td id=\"T_8ffa7_row13_col0\" class=\"data row13 col0\" >14</td>\n",
+       "      <td id=\"T_8ffa7_row13_col1\" class=\"data row13 col1\" >RandomForestClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row13_col2\" class=\"data row13 col2\" >0.736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row14_col0\" class=\"data row14 col0\" >15</td>\n",
-       "      <td id=\"T_9ea44_row14_col1\" class=\"data row14 col1\" >RobustScaler, KNeighborsClassifier</td>\n",
-       "      <td id=\"T_9ea44_row14_col2\" class=\"data row14 col2\" >0.722</td>\n",
+       "      <td id=\"T_8ffa7_row14_col0\" class=\"data row14 col0\" >15</td>\n",
+       "      <td id=\"T_8ffa7_row14_col1\" class=\"data row14 col1\" >RobustScaler, KNeighborsClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row14_col2\" class=\"data row14 col2\" >0.722</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row15_col0\" class=\"data row15 col0\" >16</td>\n",
-       "      <td id=\"T_9ea44_row15_col1\" class=\"data row15 col1\" >MaxAbsScaler, BaggingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row15_col2\" class=\"data row15 col2\" >0.694</td>\n",
+       "      <td id=\"T_8ffa7_row15_col0\" class=\"data row15 col0\" >16</td>\n",
+       "      <td id=\"T_8ffa7_row15_col1\" class=\"data row15 col1\" >MaxAbsScaler, BaggingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row15_col2\" class=\"data row15 col2\" >0.694</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row16_col0\" class=\"data row16 col0\" >17</td>\n",
-       "      <td id=\"T_9ea44_row16_col1\" class=\"data row16 col1\" >StandardScaler, BaggingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row16_col2\" class=\"data row16 col2\" >0.694</td>\n",
+       "      <td id=\"T_8ffa7_row16_col0\" class=\"data row16 col0\" >17</td>\n",
+       "      <td id=\"T_8ffa7_row16_col1\" class=\"data row16 col1\" >StandardScaler, BaggingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row16_col2\" class=\"data row16 col2\" >0.694</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row17_col0\" class=\"data row17 col0\" >18</td>\n",
-       "      <td id=\"T_9ea44_row17_col1\" class=\"data row17 col1\" >MaxAbsScaler, KNeighborsClassifier</td>\n",
-       "      <td id=\"T_9ea44_row17_col2\" class=\"data row17 col2\" >0.681</td>\n",
+       "      <td id=\"T_8ffa7_row17_col0\" class=\"data row17 col0\" >18</td>\n",
+       "      <td id=\"T_8ffa7_row17_col1\" class=\"data row17 col1\" >MaxAbsScaler, KNeighborsClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row17_col2\" class=\"data row17 col2\" >0.681</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row18_col0\" class=\"data row18 col0\" >19</td>\n",
-       "      <td id=\"T_9ea44_row18_col1\" class=\"data row18 col1\" >MaxAbsScaler, LogisticRegression</td>\n",
-       "      <td id=\"T_9ea44_row18_col2\" class=\"data row18 col2\" >0.681</td>\n",
+       "      <td id=\"T_8ffa7_row18_col0\" class=\"data row18 col0\" >19</td>\n",
+       "      <td id=\"T_8ffa7_row18_col1\" class=\"data row18 col1\" >MaxAbsScaler, LogisticRegression</td>\n",
+       "      <td id=\"T_8ffa7_row18_col2\" class=\"data row18 col2\" >0.681</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row19_col0\" class=\"data row19 col0\" >20</td>\n",
-       "      <td id=\"T_9ea44_row19_col1\" class=\"data row19 col1\" >StandardScaler, KNeighborsClassifier</td>\n",
-       "      <td id=\"T_9ea44_row19_col2\" class=\"data row19 col2\" >0.681</td>\n",
+       "      <td id=\"T_8ffa7_row19_col0\" class=\"data row19 col0\" >20</td>\n",
+       "      <td id=\"T_8ffa7_row19_col1\" class=\"data row19 col1\" >StandardScaler, KNeighborsClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row19_col2\" class=\"data row19 col2\" >0.681</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row20_col0\" class=\"data row20 col0\" >21</td>\n",
-       "      <td id=\"T_9ea44_row20_col1\" class=\"data row20 col1\" >KNeighborsClassifier</td>\n",
-       "      <td id=\"T_9ea44_row20_col2\" class=\"data row20 col2\" >0.667</td>\n",
+       "      <td id=\"T_8ffa7_row20_col0\" class=\"data row20 col0\" >21</td>\n",
+       "      <td id=\"T_8ffa7_row20_col1\" class=\"data row20 col1\" >KNeighborsClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row20_col2\" class=\"data row20 col2\" >0.667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row21_col0\" class=\"data row21 col0\" >22</td>\n",
-       "      <td id=\"T_9ea44_row21_col1\" class=\"data row21 col1\" >LogisticRegression</td>\n",
-       "      <td id=\"T_9ea44_row21_col2\" class=\"data row21 col2\" >0.639</td>\n",
+       "      <td id=\"T_8ffa7_row21_col0\" class=\"data row21 col0\" >22</td>\n",
+       "      <td id=\"T_8ffa7_row21_col1\" class=\"data row21 col1\" >LogisticRegression</td>\n",
+       "      <td id=\"T_8ffa7_row21_col2\" class=\"data row21 col2\" >0.639</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row22_col0\" class=\"data row22 col0\" >23</td>\n",
-       "      <td id=\"T_9ea44_row22_col1\" class=\"data row22 col1\" >BaggingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row22_col2\" class=\"data row22 col2\" >0.639</td>\n",
+       "      <td id=\"T_8ffa7_row22_col0\" class=\"data row22 col0\" >23</td>\n",
+       "      <td id=\"T_8ffa7_row22_col1\" class=\"data row22 col1\" >BaggingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row22_col2\" class=\"data row22 col2\" >0.639</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row23_col0\" class=\"data row23 col0\" >24</td>\n",
-       "      <td id=\"T_9ea44_row23_col1\" class=\"data row23 col1\" >StandardScaler, SGDClassifier</td>\n",
-       "      <td id=\"T_9ea44_row23_col2\" class=\"data row23 col2\" >0.639</td>\n",
+       "      <td id=\"T_8ffa7_row23_col0\" class=\"data row23 col0\" >24</td>\n",
+       "      <td id=\"T_8ffa7_row23_col1\" class=\"data row23 col1\" >StandardScaler, SGDClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row23_col2\" class=\"data row23 col2\" >0.639</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row24_col0\" class=\"data row24 col0\" >25</td>\n",
-       "      <td id=\"T_9ea44_row24_col1\" class=\"data row24 col1\" >MaxAbsScaler, LGBMClassifier</td>\n",
-       "      <td id=\"T_9ea44_row24_col2\" class=\"data row24 col2\" >0.611</td>\n",
+       "      <td id=\"T_8ffa7_row24_col0\" class=\"data row24 col0\" >25</td>\n",
+       "      <td id=\"T_8ffa7_row24_col1\" class=\"data row24 col1\" >MaxAbsScaler, LGBMClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row24_col2\" class=\"data row24 col2\" >0.611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row25_col0\" class=\"data row25 col0\" >26</td>\n",
-       "      <td id=\"T_9ea44_row25_col1\" class=\"data row25 col1\" >MaxAbsScaler, XGBClassifier</td>\n",
-       "      <td id=\"T_9ea44_row25_col2\" class=\"data row25 col2\" >0.611</td>\n",
+       "      <td id=\"T_8ffa7_row25_col0\" class=\"data row25 col0\" >26</td>\n",
+       "      <td id=\"T_8ffa7_row25_col1\" class=\"data row25 col1\" >MaxAbsScaler, XGBClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row25_col2\" class=\"data row25 col2\" >0.611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row26_col0\" class=\"data row26 col0\" >27</td>\n",
-       "      <td id=\"T_9ea44_row26_col1\" class=\"data row26 col1\" >XGBClassifier</td>\n",
-       "      <td id=\"T_9ea44_row26_col2\" class=\"data row26 col2\" >0.611</td>\n",
+       "      <td id=\"T_8ffa7_row26_col0\" class=\"data row26 col0\" >27</td>\n",
+       "      <td id=\"T_8ffa7_row26_col1\" class=\"data row26 col1\" >XGBClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row26_col2\" class=\"data row26 col2\" >0.611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row27_col0\" class=\"data row27 col0\" >28</td>\n",
-       "      <td id=\"T_9ea44_row27_col1\" class=\"data row27 col1\" >LGBMClassifier</td>\n",
-       "      <td id=\"T_9ea44_row27_col2\" class=\"data row27 col2\" >0.611</td>\n",
+       "      <td id=\"T_8ffa7_row27_col0\" class=\"data row27 col0\" >28</td>\n",
+       "      <td id=\"T_8ffa7_row27_col1\" class=\"data row27 col1\" >LGBMClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row27_col2\" class=\"data row27 col2\" >0.611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row28_col0\" class=\"data row28 col0\" >29</td>\n",
-       "      <td id=\"T_9ea44_row28_col1\" class=\"data row28 col1\" >StandardScaler, LGBMClassifier</td>\n",
-       "      <td id=\"T_9ea44_row28_col2\" class=\"data row28 col2\" >0.611</td>\n",
+       "      <td id=\"T_8ffa7_row28_col0\" class=\"data row28 col0\" >29</td>\n",
+       "      <td id=\"T_8ffa7_row28_col1\" class=\"data row28 col1\" >StandardScaler, LGBMClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row28_col2\" class=\"data row28 col2\" >0.611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row29_col0\" class=\"data row29 col0\" >30</td>\n",
-       "      <td id=\"T_9ea44_row29_col1\" class=\"data row29 col1\" >StandardScaler, XGBClassifier</td>\n",
-       "      <td id=\"T_9ea44_row29_col2\" class=\"data row29 col2\" >0.611</td>\n",
+       "      <td id=\"T_8ffa7_row29_col0\" class=\"data row29 col0\" >30</td>\n",
+       "      <td id=\"T_8ffa7_row29_col1\" class=\"data row29 col1\" >StandardScaler, XGBClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row29_col2\" class=\"data row29 col2\" >0.611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row30_col0\" class=\"data row30 col0\" >31</td>\n",
-       "      <td id=\"T_9ea44_row30_col1\" class=\"data row30 col1\" >MaxAbsScaler, DecisionTreeClassifier</td>\n",
-       "      <td id=\"T_9ea44_row30_col2\" class=\"data row30 col2\" >0.597</td>\n",
+       "      <td id=\"T_8ffa7_row30_col0\" class=\"data row30 col0\" >31</td>\n",
+       "      <td id=\"T_8ffa7_row30_col1\" class=\"data row30 col1\" >MaxAbsScaler, DecisionTreeClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row30_col2\" class=\"data row30 col2\" >0.597</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row31_col0\" class=\"data row31 col0\" >32</td>\n",
-       "      <td id=\"T_9ea44_row31_col1\" class=\"data row31 col1\" >StandardScaler, PassiveAggressiveClassifier</td>\n",
-       "      <td id=\"T_9ea44_row31_col2\" class=\"data row31 col2\" >0.597</td>\n",
+       "      <td id=\"T_8ffa7_row31_col0\" class=\"data row31 col0\" >32</td>\n",
+       "      <td id=\"T_8ffa7_row31_col1\" class=\"data row31 col1\" >StandardScaler, PassiveAggressiveClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row31_col2\" class=\"data row31 col2\" >0.597</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row32_col0\" class=\"data row32 col0\" >33</td>\n",
-       "      <td id=\"T_9ea44_row32_col1\" class=\"data row32 col1\" >GradientBoostingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row32_col2\" class=\"data row32 col2\" >0.583</td>\n",
+       "      <td id=\"T_8ffa7_row32_col0\" class=\"data row32 col0\" >33</td>\n",
+       "      <td id=\"T_8ffa7_row32_col1\" class=\"data row32 col1\" >GradientBoostingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row32_col2\" class=\"data row32 col2\" >0.583</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row33_col0\" class=\"data row33 col0\" >34</td>\n",
-       "      <td id=\"T_9ea44_row33_col1\" class=\"data row33 col1\" >StandardScaler, GradientBoostingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row33_col2\" class=\"data row33 col2\" >0.583</td>\n",
+       "      <td id=\"T_8ffa7_row33_col0\" class=\"data row33 col0\" >34</td>\n",
+       "      <td id=\"T_8ffa7_row33_col1\" class=\"data row33 col1\" >StandardScaler, GradientBoostingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row33_col2\" class=\"data row33 col2\" >0.583</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row34_col0\" class=\"data row34 col0\" >35</td>\n",
-       "      <td id=\"T_9ea44_row34_col1\" class=\"data row34 col1\" >DecisionTreeClassifier</td>\n",
-       "      <td id=\"T_9ea44_row34_col2\" class=\"data row34 col2\" >0.569</td>\n",
+       "      <td id=\"T_8ffa7_row34_col0\" class=\"data row34 col0\" >35</td>\n",
+       "      <td id=\"T_8ffa7_row34_col1\" class=\"data row34 col1\" >DecisionTreeClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row34_col2\" class=\"data row34 col2\" >0.569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row35_col0\" class=\"data row35 col0\" >36</td>\n",
-       "      <td id=\"T_9ea44_row35_col1\" class=\"data row35 col1\" >MaxAbsScaler, LinearDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row35_col2\" class=\"data row35 col2\" >0.556</td>\n",
+       "      <td id=\"T_8ffa7_row35_col0\" class=\"data row35 col0\" >36</td>\n",
+       "      <td id=\"T_8ffa7_row35_col1\" class=\"data row35 col1\" >MaxAbsScaler, LinearDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row35_col2\" class=\"data row35 col2\" >0.556</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row36_col0\" class=\"data row36 col0\" >37</td>\n",
-       "      <td id=\"T_9ea44_row36_col1\" class=\"data row36 col1\" >MaxAbsScaler, GradientBoostingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row36_col2\" class=\"data row36 col2\" >0.556</td>\n",
+       "      <td id=\"T_8ffa7_row36_col0\" class=\"data row36 col0\" >37</td>\n",
+       "      <td id=\"T_8ffa7_row36_col1\" class=\"data row36 col1\" >MaxAbsScaler, GradientBoostingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row36_col2\" class=\"data row36 col2\" >0.556</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row37_col0\" class=\"data row37 col0\" >38</td>\n",
-       "      <td id=\"T_9ea44_row37_col1\" class=\"data row37 col1\" >LinearDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row37_col2\" class=\"data row37 col2\" >0.556</td>\n",
+       "      <td id=\"T_8ffa7_row37_col0\" class=\"data row37 col0\" >38</td>\n",
+       "      <td id=\"T_8ffa7_row37_col1\" class=\"data row37 col1\" >LinearDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row37_col2\" class=\"data row37 col2\" >0.556</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row38_col0\" class=\"data row38 col0\" >39</td>\n",
-       "      <td id=\"T_9ea44_row38_col1\" class=\"data row38 col1\" >StandardScaler, LinearDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row38_col2\" class=\"data row38 col2\" >0.556</td>\n",
+       "      <td id=\"T_8ffa7_row38_col0\" class=\"data row38 col0\" >39</td>\n",
+       "      <td id=\"T_8ffa7_row38_col1\" class=\"data row38 col1\" >StandardScaler, LinearDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row38_col2\" class=\"data row38 col2\" >0.556</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row39_col0\" class=\"data row39 col0\" >40</td>\n",
-       "      <td id=\"T_9ea44_row39_col1\" class=\"data row39 col1\" >RobustScaler, LinearDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row39_col2\" class=\"data row39 col2\" >0.556</td>\n",
+       "      <td id=\"T_8ffa7_row39_col0\" class=\"data row39 col0\" >40</td>\n",
+       "      <td id=\"T_8ffa7_row39_col1\" class=\"data row39 col1\" >RobustScaler, LinearDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row39_col2\" class=\"data row39 col2\" >0.556</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row40_col0\" class=\"data row40 col0\" >41</td>\n",
-       "      <td id=\"T_9ea44_row40_col1\" class=\"data row40 col1\" >MaxAbsScaler, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row40_col2\" class=\"data row40 col2\" >0.542</td>\n",
+       "      <td id=\"T_8ffa7_row40_col0\" class=\"data row40 col0\" >41</td>\n",
+       "      <td id=\"T_8ffa7_row40_col1\" class=\"data row40 col1\" >MaxAbsScaler, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row40_col2\" class=\"data row40 col2\" >0.542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row41_col0\" class=\"data row41 col0\" >42</td>\n",
-       "      <td id=\"T_9ea44_row41_col1\" class=\"data row41 col1\" >GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row41_col2\" class=\"data row41 col2\" >0.542</td>\n",
+       "      <td id=\"T_8ffa7_row41_col0\" class=\"data row41 col0\" >42</td>\n",
+       "      <td id=\"T_8ffa7_row41_col1\" class=\"data row41 col1\" >GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row41_col2\" class=\"data row41 col2\" >0.542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row42_col0\" class=\"data row42 col0\" >43</td>\n",
-       "      <td id=\"T_9ea44_row42_col1\" class=\"data row42 col1\" >StandardScaler, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row42_col2\" class=\"data row42 col2\" >0.542</td>\n",
+       "      <td id=\"T_8ffa7_row42_col0\" class=\"data row42 col0\" >43</td>\n",
+       "      <td id=\"T_8ffa7_row42_col1\" class=\"data row42 col1\" >StandardScaler, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row42_col2\" class=\"data row42 col2\" >0.542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row43_col0\" class=\"data row43 col0\" >44</td>\n",
-       "      <td id=\"T_9ea44_row43_col1\" class=\"data row43 col1\" >RobustScaler, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row43_col2\" class=\"data row43 col2\" >0.542</td>\n",
+       "      <td id=\"T_8ffa7_row43_col0\" class=\"data row43 col0\" >44</td>\n",
+       "      <td id=\"T_8ffa7_row43_col1\" class=\"data row43 col1\" >RobustScaler, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row43_col2\" class=\"data row43 col2\" >0.542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row44_col0\" class=\"data row44 col0\" >45</td>\n",
-       "      <td id=\"T_9ea44_row44_col1\" class=\"data row44 col1\" >MaxAbsScaler, MultinomialNB</td>\n",
-       "      <td id=\"T_9ea44_row44_col2\" class=\"data row44 col2\" >0.542</td>\n",
+       "      <td id=\"T_8ffa7_row44_col0\" class=\"data row44 col0\" >45</td>\n",
+       "      <td id=\"T_8ffa7_row44_col1\" class=\"data row44 col1\" >MaxAbsScaler, MultinomialNB</td>\n",
+       "      <td id=\"T_8ffa7_row44_col2\" class=\"data row44 col2\" >0.542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row45_col0\" class=\"data row45 col0\" >46</td>\n",
-       "      <td id=\"T_9ea44_row45_col1\" class=\"data row45 col1\" >MaxAbsScaler, PassiveAggressiveClassifier</td>\n",
-       "      <td id=\"T_9ea44_row45_col2\" class=\"data row45 col2\" >0.542</td>\n",
+       "      <td id=\"T_8ffa7_row45_col0\" class=\"data row45 col0\" >46</td>\n",
+       "      <td id=\"T_8ffa7_row45_col1\" class=\"data row45 col1\" >MaxAbsScaler, PassiveAggressiveClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row45_col2\" class=\"data row45 col2\" >0.542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row46_col0\" class=\"data row46 col0\" >47</td>\n",
-       "      <td id=\"T_9ea44_row46_col1\" class=\"data row46 col1\" >MultinomialNB</td>\n",
-       "      <td id=\"T_9ea44_row46_col2\" class=\"data row46 col2\" >0.542</td>\n",
+       "      <td id=\"T_8ffa7_row46_col0\" class=\"data row46 col0\" >47</td>\n",
+       "      <td id=\"T_8ffa7_row46_col1\" class=\"data row46 col1\" >MultinomialNB</td>\n",
+       "      <td id=\"T_8ffa7_row46_col2\" class=\"data row46 col2\" >0.542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row47_col0\" class=\"data row47 col0\" >48</td>\n",
-       "      <td id=\"T_9ea44_row47_col1\" class=\"data row47 col1\" >SGDClassifier</td>\n",
-       "      <td id=\"T_9ea44_row47_col2\" class=\"data row47 col2\" >0.542</td>\n",
+       "      <td id=\"T_8ffa7_row47_col0\" class=\"data row47 col0\" >48</td>\n",
+       "      <td id=\"T_8ffa7_row47_col1\" class=\"data row47 col1\" >SGDClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row47_col2\" class=\"data row47 col2\" >0.542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row48_col0\" class=\"data row48 col0\" >49</td>\n",
-       "      <td id=\"T_9ea44_row48_col1\" class=\"data row48 col1\" >StandardScaler, BernoulliNB</td>\n",
-       "      <td id=\"T_9ea44_row48_col2\" class=\"data row48 col2\" >0.528</td>\n",
+       "      <td id=\"T_8ffa7_row48_col0\" class=\"data row48 col0\" >49</td>\n",
+       "      <td id=\"T_8ffa7_row48_col1\" class=\"data row48 col1\" >StandardScaler, BernoulliNB</td>\n",
+       "      <td id=\"T_8ffa7_row48_col2\" class=\"data row48 col2\" >0.528</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row49_col0\" class=\"data row49 col0\" >50</td>\n",
-       "      <td id=\"T_9ea44_row49_col1\" class=\"data row49 col1\" >StandardScaler, DecisionTreeClassifier</td>\n",
-       "      <td id=\"T_9ea44_row49_col2\" class=\"data row49 col2\" >0.528</td>\n",
+       "      <td id=\"T_8ffa7_row49_col0\" class=\"data row49 col0\" >50</td>\n",
+       "      <td id=\"T_8ffa7_row49_col1\" class=\"data row49 col1\" >StandardScaler, DecisionTreeClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row49_col2\" class=\"data row49 col2\" >0.528</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row50_col0\" class=\"data row50 col0\" >51</td>\n",
-       "      <td id=\"T_9ea44_row50_col1\" class=\"data row50 col1\" >MaxAbsScaler, SelectKBest, RandomForestClassifier</td>\n",
-       "      <td id=\"T_9ea44_row50_col2\" class=\"data row50 col2\" >0.514</td>\n",
+       "      <td id=\"T_8ffa7_row50_col0\" class=\"data row50 col0\" >51</td>\n",
+       "      <td id=\"T_8ffa7_row50_col1\" class=\"data row50 col1\" >MaxAbsScaler, SelectKBest, RandomForestClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row50_col2\" class=\"data row50 col2\" >0.514</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row51_col0\" class=\"data row51 col0\" >52</td>\n",
-       "      <td id=\"T_9ea44_row51_col1\" class=\"data row51 col1\" >StandardScaler, SelectKBest, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row51_col2\" class=\"data row51 col2\" >0.500</td>\n",
+       "      <td id=\"T_8ffa7_row51_col0\" class=\"data row51 col0\" >52</td>\n",
+       "      <td id=\"T_8ffa7_row51_col1\" class=\"data row51 col1\" >StandardScaler, SelectKBest, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row51_col2\" class=\"data row51 col2\" >0.500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row52_col0\" class=\"data row52 col0\" >53</td>\n",
-       "      <td id=\"T_9ea44_row52_col1\" class=\"data row52 col1\" >RobustScaler, SelectKBest, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row52_col2\" class=\"data row52 col2\" >0.500</td>\n",
+       "      <td id=\"T_8ffa7_row52_col0\" class=\"data row52 col0\" >53</td>\n",
+       "      <td id=\"T_8ffa7_row52_col1\" class=\"data row52 col1\" >RobustScaler, SelectKBest, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row52_col2\" class=\"data row52 col2\" >0.500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row53_col0\" class=\"data row53 col0\" >54</td>\n",
-       "      <td id=\"T_9ea44_row53_col1\" class=\"data row53 col1\" >MaxAbsScaler, SelectPercentile, XGBClassifier</td>\n",
-       "      <td id=\"T_9ea44_row53_col2\" class=\"data row53 col2\" >0.486</td>\n",
+       "      <td id=\"T_8ffa7_row53_col0\" class=\"data row53 col0\" >54</td>\n",
+       "      <td id=\"T_8ffa7_row53_col1\" class=\"data row53 col1\" >MaxAbsScaler, SelectPercentile, XGBClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row53_col2\" class=\"data row53 col2\" >0.486</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row54_col0\" class=\"data row54 col0\" >55</td>\n",
-       "      <td id=\"T_9ea44_row54_col1\" class=\"data row54 col1\" >MaxAbsScaler, SelectKBest, XGBClassifier</td>\n",
-       "      <td id=\"T_9ea44_row54_col2\" class=\"data row54 col2\" >0.472</td>\n",
+       "      <td id=\"T_8ffa7_row54_col0\" class=\"data row54 col0\" >55</td>\n",
+       "      <td id=\"T_8ffa7_row54_col1\" class=\"data row54 col1\" >MaxAbsScaler, SelectKBest, XGBClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row54_col2\" class=\"data row54 col2\" >0.472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row55_col0\" class=\"data row55 col0\" >56</td>\n",
-       "      <td id=\"T_9ea44_row55_col1\" class=\"data row55 col1\" >PassiveAggressiveClassifier</td>\n",
-       "      <td id=\"T_9ea44_row55_col2\" class=\"data row55 col2\" >0.472</td>\n",
+       "      <td id=\"T_8ffa7_row55_col0\" class=\"data row55 col0\" >56</td>\n",
+       "      <td id=\"T_8ffa7_row55_col1\" class=\"data row55 col1\" >PassiveAggressiveClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row55_col2\" class=\"data row55 col2\" >0.472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row56_col0\" class=\"data row56 col0\" >57</td>\n",
-       "      <td id=\"T_9ea44_row56_col1\" class=\"data row56 col1\" >MaxAbsScaler, SelectKBest, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row56_col2\" class=\"data row56 col2\" >0.458</td>\n",
+       "      <td id=\"T_8ffa7_row56_col0\" class=\"data row56 col0\" >57</td>\n",
+       "      <td id=\"T_8ffa7_row56_col1\" class=\"data row56 col1\" >MaxAbsScaler, SelectKBest, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row56_col2\" class=\"data row56 col2\" >0.458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row57_col0\" class=\"data row57 col0\" >58</td>\n",
-       "      <td id=\"T_9ea44_row57_col1\" class=\"data row57 col1\" >MaxAbsScaler, SelectPercentile, DecisionTreeClassifier</td>\n",
-       "      <td id=\"T_9ea44_row57_col2\" class=\"data row57 col2\" >0.458</td>\n",
+       "      <td id=\"T_8ffa7_row57_col0\" class=\"data row57 col0\" >58</td>\n",
+       "      <td id=\"T_8ffa7_row57_col1\" class=\"data row57 col1\" >MaxAbsScaler, SelectPercentile, DecisionTreeClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row57_col2\" class=\"data row57 col2\" >0.458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row58_col0\" class=\"data row58 col0\" >59</td>\n",
-       "      <td id=\"T_9ea44_row58_col1\" class=\"data row58 col1\" >MaxAbsScaler, SelectPercentile, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row58_col2\" class=\"data row58 col2\" >0.444</td>\n",
+       "      <td id=\"T_8ffa7_row58_col0\" class=\"data row58 col0\" >59</td>\n",
+       "      <td id=\"T_8ffa7_row58_col1\" class=\"data row58 col1\" >MaxAbsScaler, SelectPercentile, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row58_col2\" class=\"data row58 col2\" >0.444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row59_col0\" class=\"data row59 col0\" >60</td>\n",
-       "      <td id=\"T_9ea44_row59_col1\" class=\"data row59 col1\" >SelectPercentile, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row59_col2\" class=\"data row59 col2\" >0.444</td>\n",
+       "      <td id=\"T_8ffa7_row59_col0\" class=\"data row59 col0\" >60</td>\n",
+       "      <td id=\"T_8ffa7_row59_col1\" class=\"data row59 col1\" >SelectPercentile, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row59_col2\" class=\"data row59 col2\" >0.444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row60_col0\" class=\"data row60 col0\" >61</td>\n",
-       "      <td id=\"T_9ea44_row60_col1\" class=\"data row60 col1\" >SelectKBest, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row60_col2\" class=\"data row60 col2\" >0.444</td>\n",
+       "      <td id=\"T_8ffa7_row60_col0\" class=\"data row60 col0\" >61</td>\n",
+       "      <td id=\"T_8ffa7_row60_col1\" class=\"data row60 col1\" >SelectKBest, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row60_col2\" class=\"data row60 col2\" >0.444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row61_col0\" class=\"data row61 col0\" >62</td>\n",
-       "      <td id=\"T_9ea44_row61_col1\" class=\"data row61 col1\" >MaxAbsScaler, SelectPercentile, RandomForestClassifier</td>\n",
-       "      <td id=\"T_9ea44_row61_col2\" class=\"data row61 col2\" >0.444</td>\n",
+       "      <td id=\"T_8ffa7_row61_col0\" class=\"data row61 col0\" >62</td>\n",
+       "      <td id=\"T_8ffa7_row61_col1\" class=\"data row61 col1\" >MaxAbsScaler, SelectPercentile, RandomForestClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row61_col2\" class=\"data row61 col2\" >0.444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row62_col0\" class=\"data row62 col0\" >63</td>\n",
-       "      <td id=\"T_9ea44_row62_col1\" class=\"data row62 col1\" >MaxAbsScaler, SelectPercentile, LGBMClassifier</td>\n",
-       "      <td id=\"T_9ea44_row62_col2\" class=\"data row62 col2\" >0.444</td>\n",
+       "      <td id=\"T_8ffa7_row62_col0\" class=\"data row62 col0\" >63</td>\n",
+       "      <td id=\"T_8ffa7_row62_col1\" class=\"data row62 col1\" >MaxAbsScaler, SelectPercentile, LGBMClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row62_col2\" class=\"data row62 col2\" >0.444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row63_col0\" class=\"data row63 col0\" >64</td>\n",
-       "      <td id=\"T_9ea44_row63_col1\" class=\"data row63 col1\" >MaxAbsScaler, SelectKBest, DecisionTreeClassifier</td>\n",
-       "      <td id=\"T_9ea44_row63_col2\" class=\"data row63 col2\" >0.444</td>\n",
+       "      <td id=\"T_8ffa7_row63_col0\" class=\"data row63 col0\" >64</td>\n",
+       "      <td id=\"T_8ffa7_row63_col1\" class=\"data row63 col1\" >MaxAbsScaler, SelectKBest, DecisionTreeClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row63_col2\" class=\"data row63 col2\" >0.444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row64_col0\" class=\"data row64 col0\" >65</td>\n",
-       "      <td id=\"T_9ea44_row64_col1\" class=\"data row64 col1\" >MaxAbsScaler, SelectKBest, BaggingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row64_col2\" class=\"data row64 col2\" >0.444</td>\n",
+       "      <td id=\"T_8ffa7_row64_col0\" class=\"data row64 col0\" >65</td>\n",
+       "      <td id=\"T_8ffa7_row64_col1\" class=\"data row64 col1\" >MaxAbsScaler, SelectKBest, BaggingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row64_col2\" class=\"data row64 col2\" >0.444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row65_col0\" class=\"data row65 col0\" >66</td>\n",
-       "      <td id=\"T_9ea44_row65_col1\" class=\"data row65 col1\" >RobustScaler, SelectPercentile, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row65_col2\" class=\"data row65 col2\" >0.431</td>\n",
+       "      <td id=\"T_8ffa7_row65_col0\" class=\"data row65 col0\" >66</td>\n",
+       "      <td id=\"T_8ffa7_row65_col1\" class=\"data row65 col1\" >RobustScaler, SelectPercentile, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row65_col2\" class=\"data row65 col2\" >0.431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row66_col0\" class=\"data row66 col0\" >67</td>\n",
-       "      <td id=\"T_9ea44_row66_col1\" class=\"data row66 col1\" >MaxAbsScaler, SGDClassifier</td>\n",
-       "      <td id=\"T_9ea44_row66_col2\" class=\"data row66 col2\" >0.431</td>\n",
+       "      <td id=\"T_8ffa7_row66_col0\" class=\"data row66 col0\" >67</td>\n",
+       "      <td id=\"T_8ffa7_row66_col1\" class=\"data row66 col1\" >MaxAbsScaler, SGDClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row66_col2\" class=\"data row66 col2\" >0.431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row67_col0\" class=\"data row67 col0\" >68</td>\n",
-       "      <td id=\"T_9ea44_row67_col1\" class=\"data row67 col1\" >MaxAbsScaler, SelectPercentile, QuadraticDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row67_col2\" class=\"data row67 col2\" >0.431</td>\n",
+       "      <td id=\"T_8ffa7_row67_col0\" class=\"data row67 col0\" >68</td>\n",
+       "      <td id=\"T_8ffa7_row67_col1\" class=\"data row67 col1\" >MaxAbsScaler, SelectPercentile, QuadraticDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row67_col2\" class=\"data row67 col2\" >0.431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row68_col0\" class=\"data row68 col0\" >69</td>\n",
-       "      <td id=\"T_9ea44_row68_col1\" class=\"data row68 col1\" >MaxAbsScaler, SelectPercentile, BaggingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row68_col2\" class=\"data row68 col2\" >0.431</td>\n",
+       "      <td id=\"T_8ffa7_row68_col0\" class=\"data row68 col0\" >69</td>\n",
+       "      <td id=\"T_8ffa7_row68_col1\" class=\"data row68 col1\" >MaxAbsScaler, SelectPercentile, BaggingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row68_col2\" class=\"data row68 col2\" >0.431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row69_col0\" class=\"data row69 col0\" >70</td>\n",
-       "      <td id=\"T_9ea44_row69_col1\" class=\"data row69 col1\" >MaxAbsScaler, SelectKBest, LGBMClassifier</td>\n",
-       "      <td id=\"T_9ea44_row69_col2\" class=\"data row69 col2\" >0.431</td>\n",
+       "      <td id=\"T_8ffa7_row69_col0\" class=\"data row69 col0\" >70</td>\n",
+       "      <td id=\"T_8ffa7_row69_col1\" class=\"data row69 col1\" >MaxAbsScaler, SelectKBest, LGBMClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row69_col2\" class=\"data row69 col2\" >0.431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row70_col0\" class=\"data row70 col0\" >71</td>\n",
-       "      <td id=\"T_9ea44_row70_col1\" class=\"data row70 col1\" >MaxAbsScaler, SelectPercentile, KNeighborsClassifier</td>\n",
-       "      <td id=\"T_9ea44_row70_col2\" class=\"data row70 col2\" >0.417</td>\n",
+       "      <td id=\"T_8ffa7_row70_col0\" class=\"data row70 col0\" >71</td>\n",
+       "      <td id=\"T_8ffa7_row70_col1\" class=\"data row70 col1\" >MaxAbsScaler, SelectPercentile, KNeighborsClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row70_col2\" class=\"data row70 col2\" >0.417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row71_col0\" class=\"data row71 col0\" >72</td>\n",
-       "      <td id=\"T_9ea44_row71_col1\" class=\"data row71 col1\" >StandardScaler, SelectPercentile, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row71_col2\" class=\"data row71 col2\" >0.417</td>\n",
+       "      <td id=\"T_8ffa7_row71_col0\" class=\"data row71 col0\" >72</td>\n",
+       "      <td id=\"T_8ffa7_row71_col1\" class=\"data row71 col1\" >StandardScaler, SelectPercentile, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row71_col2\" class=\"data row71 col2\" >0.417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row72_col0\" class=\"data row72 col0\" >73</td>\n",
-       "      <td id=\"T_9ea44_row72_col1\" class=\"data row72 col1\" >MaxAbsScaler, SelectKBest, KNeighborsClassifier</td>\n",
-       "      <td id=\"T_9ea44_row72_col2\" class=\"data row72 col2\" >0.417</td>\n",
+       "      <td id=\"T_8ffa7_row72_col0\" class=\"data row72 col0\" >73</td>\n",
+       "      <td id=\"T_8ffa7_row72_col1\" class=\"data row72 col1\" >MaxAbsScaler, SelectKBest, KNeighborsClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row72_col2\" class=\"data row72 col2\" >0.417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row73_col0\" class=\"data row73 col0\" >74</td>\n",
-       "      <td id=\"T_9ea44_row73_col1\" class=\"data row73 col1\" >MaxAbsScaler, SelectKBest, GradientBoostingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row73_col2\" class=\"data row73 col2\" >0.417</td>\n",
+       "      <td id=\"T_8ffa7_row73_col0\" class=\"data row73 col0\" >74</td>\n",
+       "      <td id=\"T_8ffa7_row73_col1\" class=\"data row73 col1\" >MaxAbsScaler, SelectKBest, GradientBoostingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row73_col2\" class=\"data row73 col2\" >0.417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row74_col0\" class=\"data row74 col0\" >75</td>\n",
-       "      <td id=\"T_9ea44_row74_col1\" class=\"data row74 col1\" >StandardScaler, QuadraticDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row74_col2\" class=\"data row74 col2\" >0.417</td>\n",
+       "      <td id=\"T_8ffa7_row74_col0\" class=\"data row74 col0\" >75</td>\n",
+       "      <td id=\"T_8ffa7_row74_col1\" class=\"data row74 col1\" >StandardScaler, QuadraticDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row74_col2\" class=\"data row74 col2\" >0.417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row75_col0\" class=\"data row75 col0\" >76</td>\n",
-       "      <td id=\"T_9ea44_row75_col1\" class=\"data row75 col1\" >MaxAbsScaler, QuadraticDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row75_col2\" class=\"data row75 col2\" >0.403</td>\n",
+       "      <td id=\"T_8ffa7_row75_col0\" class=\"data row75 col0\" >76</td>\n",
+       "      <td id=\"T_8ffa7_row75_col1\" class=\"data row75 col1\" >MaxAbsScaler, QuadraticDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row75_col2\" class=\"data row75 col2\" >0.403</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row76_col0\" class=\"data row76 col0\" >77</td>\n",
-       "      <td id=\"T_9ea44_row76_col1\" class=\"data row76 col1\" >SelectPercentile, KNeighborsClassifier</td>\n",
-       "      <td id=\"T_9ea44_row76_col2\" class=\"data row76 col2\" >0.403</td>\n",
+       "      <td id=\"T_8ffa7_row76_col0\" class=\"data row76 col0\" >77</td>\n",
+       "      <td id=\"T_8ffa7_row76_col1\" class=\"data row76 col1\" >SelectPercentile, KNeighborsClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row76_col2\" class=\"data row76 col2\" >0.403</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row77_col0\" class=\"data row77 col0\" >78</td>\n",
-       "      <td id=\"T_9ea44_row77_col1\" class=\"data row77 col1\" >MaxAbsScaler, SelectPercentile, GradientBoostingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row77_col2\" class=\"data row77 col2\" >0.375</td>\n",
+       "      <td id=\"T_8ffa7_row77_col0\" class=\"data row77 col0\" >78</td>\n",
+       "      <td id=\"T_8ffa7_row77_col1\" class=\"data row77 col1\" >MaxAbsScaler, SelectPercentile, GradientBoostingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row77_col2\" class=\"data row77 col2\" >0.375</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row78_col0\" class=\"data row78 col0\" >79</td>\n",
-       "      <td id=\"T_9ea44_row78_col1\" class=\"data row78 col1\" >MaxAbsScaler, SelectKBest, QuadraticDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row78_col2\" class=\"data row78 col2\" >0.375</td>\n",
+       "      <td id=\"T_8ffa7_row78_col0\" class=\"data row78 col0\" >79</td>\n",
+       "      <td id=\"T_8ffa7_row78_col1\" class=\"data row78 col1\" >MaxAbsScaler, SelectKBest, QuadraticDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row78_col2\" class=\"data row78 col2\" >0.375</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row79_col0\" class=\"data row79 col0\" >80</td>\n",
-       "      <td id=\"T_9ea44_row79_col1\" class=\"data row79 col1\" >QuadraticDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row79_col2\" class=\"data row79 col2\" >0.361</td>\n",
+       "      <td id=\"T_8ffa7_row79_col0\" class=\"data row79 col0\" >80</td>\n",
+       "      <td id=\"T_8ffa7_row79_col1\" class=\"data row79 col1\" >QuadraticDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row79_col2\" class=\"data row79 col2\" >0.361</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row80_col0\" class=\"data row80 col0\" >81</td>\n",
-       "      <td id=\"T_9ea44_row80_col1\" class=\"data row80 col1\" >MaxAbsScaler, SelectKBest, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row80_col2\" class=\"data row80 col2\" >0.347</td>\n",
+       "      <td id=\"T_8ffa7_row80_col0\" class=\"data row80 col0\" >81</td>\n",
+       "      <td id=\"T_8ffa7_row80_col1\" class=\"data row80 col1\" >MaxAbsScaler, SelectKBest, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row80_col2\" class=\"data row80 col2\" >0.347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row81_col0\" class=\"data row81 col0\" >82</td>\n",
-       "      <td id=\"T_9ea44_row81_col1\" class=\"data row81 col1\" >SelectKBest, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row81_col2\" class=\"data row81 col2\" >0.347</td>\n",
+       "      <td id=\"T_8ffa7_row81_col0\" class=\"data row81 col0\" >82</td>\n",
+       "      <td id=\"T_8ffa7_row81_col1\" class=\"data row81 col1\" >SelectKBest, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row81_col2\" class=\"data row81 col2\" >0.347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row82_col0\" class=\"data row82 col0\" >83</td>\n",
-       "      <td id=\"T_9ea44_row82_col1\" class=\"data row82 col1\" >StandardScaler, SelectKBest, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row82_col2\" class=\"data row82 col2\" >0.347</td>\n",
+       "      <td id=\"T_8ffa7_row82_col0\" class=\"data row82 col0\" >83</td>\n",
+       "      <td id=\"T_8ffa7_row82_col1\" class=\"data row82 col1\" >StandardScaler, SelectKBest, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row82_col2\" class=\"data row82 col2\" >0.347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row83_col0\" class=\"data row83 col0\" >84</td>\n",
-       "      <td id=\"T_9ea44_row83_col1\" class=\"data row83 col1\" >RobustScaler, SelectKBest, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row83_col2\" class=\"data row83 col2\" >0.347</td>\n",
+       "      <td id=\"T_8ffa7_row83_col0\" class=\"data row83 col0\" >84</td>\n",
+       "      <td id=\"T_8ffa7_row83_col1\" class=\"data row83 col1\" >RobustScaler, SelectKBest, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row83_col2\" class=\"data row83 col2\" >0.347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row84_col0\" class=\"data row84 col0\" >85</td>\n",
-       "      <td id=\"T_9ea44_row84_col1\" class=\"data row84 col1\" >MaxAbsScaler, SelectPercentile, SVC</td>\n",
-       "      <td id=\"T_9ea44_row84_col2\" class=\"data row84 col2\" >0.333</td>\n",
+       "      <td id=\"T_8ffa7_row84_col0\" class=\"data row84 col0\" >85</td>\n",
+       "      <td id=\"T_8ffa7_row84_col1\" class=\"data row84 col1\" >MaxAbsScaler, SelectPercentile, SVC</td>\n",
+       "      <td id=\"T_8ffa7_row84_col2\" class=\"data row84 col2\" >0.333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row85_col0\" class=\"data row85 col0\" >86</td>\n",
-       "      <td id=\"T_9ea44_row85_col1\" class=\"data row85 col1\" >MaxAbsScaler, SelectPercentile, LinearDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row85_col2\" class=\"data row85 col2\" >0.319</td>\n",
+       "      <td id=\"T_8ffa7_row85_col0\" class=\"data row85 col0\" >86</td>\n",
+       "      <td id=\"T_8ffa7_row85_col1\" class=\"data row85 col1\" >MaxAbsScaler, SelectPercentile, LinearDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row85_col2\" class=\"data row85 col2\" >0.319</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row86_col0\" class=\"data row86 col0\" >87</td>\n",
-       "      <td id=\"T_9ea44_row86_col1\" class=\"data row86 col1\" >MaxAbsScaler, SelectKBest, LinearDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row86_col2\" class=\"data row86 col2\" >0.319</td>\n",
+       "      <td id=\"T_8ffa7_row86_col0\" class=\"data row86 col0\" >87</td>\n",
+       "      <td id=\"T_8ffa7_row86_col1\" class=\"data row86 col1\" >MaxAbsScaler, SelectKBest, LinearDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row86_col2\" class=\"data row86 col2\" >0.319</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row87_col0\" class=\"data row87 col0\" >88</td>\n",
-       "      <td id=\"T_9ea44_row87_col1\" class=\"data row87 col1\" >MaxAbsScaler, SelectKBest, LinearSVC</td>\n",
-       "      <td id=\"T_9ea44_row87_col2\" class=\"data row87 col2\" >0.306</td>\n",
+       "      <td id=\"T_8ffa7_row87_col0\" class=\"data row87 col0\" >88</td>\n",
+       "      <td id=\"T_8ffa7_row87_col1\" class=\"data row87 col1\" >MaxAbsScaler, SelectKBest, LinearSVC</td>\n",
+       "      <td id=\"T_8ffa7_row87_col2\" class=\"data row87 col2\" >0.306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row88_col0\" class=\"data row88 col0\" >89</td>\n",
-       "      <td id=\"T_9ea44_row88_col1\" class=\"data row88 col1\" >MaxAbsScaler, SelectKBest, SVC</td>\n",
-       "      <td id=\"T_9ea44_row88_col2\" class=\"data row88 col2\" >0.306</td>\n",
+       "      <td id=\"T_8ffa7_row88_col0\" class=\"data row88 col0\" >89</td>\n",
+       "      <td id=\"T_8ffa7_row88_col1\" class=\"data row88 col1\" >MaxAbsScaler, SelectKBest, SVC</td>\n",
+       "      <td id=\"T_8ffa7_row88_col2\" class=\"data row88 col2\" >0.306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row89_col0\" class=\"data row89 col0\" >90</td>\n",
-       "      <td id=\"T_9ea44_row89_col1\" class=\"data row89 col1\" >MaxAbsScaler, SelectPercentile, LinearSVC</td>\n",
-       "      <td id=\"T_9ea44_row89_col2\" class=\"data row89 col2\" >0.292</td>\n",
+       "      <td id=\"T_8ffa7_row89_col0\" class=\"data row89 col0\" >90</td>\n",
+       "      <td id=\"T_8ffa7_row89_col1\" class=\"data row89 col1\" >MaxAbsScaler, SelectPercentile, LinearSVC</td>\n",
+       "      <td id=\"T_8ffa7_row89_col2\" class=\"data row89 col2\" >0.292</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row90_col0\" class=\"data row90 col0\" >91</td>\n",
-       "      <td id=\"T_9ea44_row90_col1\" class=\"data row90 col1\" >MaxAbsScaler, SelectKBest, LogisticRegression</td>\n",
-       "      <td id=\"T_9ea44_row90_col2\" class=\"data row90 col2\" >0.278</td>\n",
+       "      <td id=\"T_8ffa7_row90_col0\" class=\"data row90 col0\" >91</td>\n",
+       "      <td id=\"T_8ffa7_row90_col1\" class=\"data row90 col1\" >MaxAbsScaler, SelectKBest, LogisticRegression</td>\n",
+       "      <td id=\"T_8ffa7_row90_col2\" class=\"data row90 col2\" >0.278</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row91_col0\" class=\"data row91 col0\" >92</td>\n",
-       "      <td id=\"T_9ea44_row91_col1\" class=\"data row91 col1\" >MaxAbsScaler, GenericUnivariateSelect, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row91_col2\" class=\"data row91 col2\" >0.264</td>\n",
+       "      <td id=\"T_8ffa7_row91_col0\" class=\"data row91 col0\" >92</td>\n",
+       "      <td id=\"T_8ffa7_row91_col1\" class=\"data row91 col1\" >MaxAbsScaler, GenericUnivariateSelect, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row91_col2\" class=\"data row91 col2\" >0.264</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row92_col0\" class=\"data row92 col0\" >93</td>\n",
-       "      <td id=\"T_9ea44_row92_col1\" class=\"data row92 col1\" >RobustScaler, GenericUnivariateSelect, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row92_col2\" class=\"data row92 col2\" >0.264</td>\n",
+       "      <td id=\"T_8ffa7_row92_col0\" class=\"data row92 col0\" >93</td>\n",
+       "      <td id=\"T_8ffa7_row92_col1\" class=\"data row92 col1\" >RobustScaler, GenericUnivariateSelect, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row92_col2\" class=\"data row92 col2\" >0.264</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row93_col0\" class=\"data row93 col0\" >94</td>\n",
-       "      <td id=\"T_9ea44_row93_col1\" class=\"data row93 col1\" >MaxAbsScaler, GenericUnivariateSelect, DecisionTreeClassifier</td>\n",
-       "      <td id=\"T_9ea44_row93_col2\" class=\"data row93 col2\" >0.250</td>\n",
+       "      <td id=\"T_8ffa7_row93_col0\" class=\"data row93 col0\" >94</td>\n",
+       "      <td id=\"T_8ffa7_row93_col1\" class=\"data row93 col1\" >MaxAbsScaler, GenericUnivariateSelect, DecisionTreeClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row93_col2\" class=\"data row93 col2\" >0.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row94_col0\" class=\"data row94 col0\" >95</td>\n",
-       "      <td id=\"T_9ea44_row94_col1\" class=\"data row94 col1\" >MaxAbsScaler, SelectPercentile, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row94_col2\" class=\"data row94 col2\" >0.250</td>\n",
+       "      <td id=\"T_8ffa7_row94_col0\" class=\"data row94 col0\" >95</td>\n",
+       "      <td id=\"T_8ffa7_row94_col1\" class=\"data row94 col1\" >MaxAbsScaler, SelectPercentile, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row94_col2\" class=\"data row94 col2\" >0.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row95_col0\" class=\"data row95 col0\" >96</td>\n",
-       "      <td id=\"T_9ea44_row95_col1\" class=\"data row95 col1\" >GenericUnivariateSelect, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row95_col2\" class=\"data row95 col2\" >0.250</td>\n",
+       "      <td id=\"T_8ffa7_row95_col0\" class=\"data row95 col0\" >96</td>\n",
+       "      <td id=\"T_8ffa7_row95_col1\" class=\"data row95 col1\" >GenericUnivariateSelect, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row95_col2\" class=\"data row95 col2\" >0.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row96_col0\" class=\"data row96 col0\" >97</td>\n",
-       "      <td id=\"T_9ea44_row96_col1\" class=\"data row96 col1\" >StandardScaler, GenericUnivariateSelect, ExtraTreesClassifier</td>\n",
-       "      <td id=\"T_9ea44_row96_col2\" class=\"data row96 col2\" >0.250</td>\n",
+       "      <td id=\"T_8ffa7_row96_col0\" class=\"data row96 col0\" >97</td>\n",
+       "      <td id=\"T_8ffa7_row96_col1\" class=\"data row96 col1\" >StandardScaler, GenericUnivariateSelect, ExtraTreesClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row96_col2\" class=\"data row96 col2\" >0.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row97_col0\" class=\"data row97 col0\" >98</td>\n",
-       "      <td id=\"T_9ea44_row97_col1\" class=\"data row97 col1\" >SelectPercentile, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row97_col2\" class=\"data row97 col2\" >0.250</td>\n",
+       "      <td id=\"T_8ffa7_row97_col0\" class=\"data row97 col0\" >98</td>\n",
+       "      <td id=\"T_8ffa7_row97_col1\" class=\"data row97 col1\" >SelectPercentile, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row97_col2\" class=\"data row97 col2\" >0.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row98_col0\" class=\"data row98 col0\" >99</td>\n",
-       "      <td id=\"T_9ea44_row98_col1\" class=\"data row98 col1\" >StandardScaler, SelectPercentile, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row98_col2\" class=\"data row98 col2\" >0.250</td>\n",
+       "      <td id=\"T_8ffa7_row98_col0\" class=\"data row98 col0\" >99</td>\n",
+       "      <td id=\"T_8ffa7_row98_col1\" class=\"data row98 col1\" >StandardScaler, SelectPercentile, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row98_col2\" class=\"data row98 col2\" >0.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row99_col0\" class=\"data row99 col0\" >100</td>\n",
-       "      <td id=\"T_9ea44_row99_col1\" class=\"data row99 col1\" >RobustScaler, SelectPercentile, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row99_col2\" class=\"data row99 col2\" >0.250</td>\n",
+       "      <td id=\"T_8ffa7_row99_col0\" class=\"data row99 col0\" >100</td>\n",
+       "      <td id=\"T_8ffa7_row99_col1\" class=\"data row99 col1\" >RobustScaler, SelectPercentile, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row99_col2\" class=\"data row99 col2\" >0.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row100_col0\" class=\"data row100 col0\" >101</td>\n",
-       "      <td id=\"T_9ea44_row100_col1\" class=\"data row100 col1\" >MaxAbsScaler, SelectPercentile, LogisticRegression</td>\n",
-       "      <td id=\"T_9ea44_row100_col2\" class=\"data row100 col2\" >0.250</td>\n",
+       "      <td id=\"T_8ffa7_row100_col0\" class=\"data row100 col0\" >101</td>\n",
+       "      <td id=\"T_8ffa7_row100_col1\" class=\"data row100 col1\" >MaxAbsScaler, SelectPercentile, LogisticRegression</td>\n",
+       "      <td id=\"T_8ffa7_row100_col2\" class=\"data row100 col2\" >0.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row101_col0\" class=\"data row101 col0\" >102</td>\n",
-       "      <td id=\"T_9ea44_row101_col1\" class=\"data row101 col1\" >GenericUnivariateSelect, DecisionTreeClassifier</td>\n",
-       "      <td id=\"T_9ea44_row101_col2\" class=\"data row101 col2\" >0.250</td>\n",
+       "      <td id=\"T_8ffa7_row101_col0\" class=\"data row101 col0\" >102</td>\n",
+       "      <td id=\"T_8ffa7_row101_col1\" class=\"data row101 col1\" >GenericUnivariateSelect, DecisionTreeClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row101_col2\" class=\"data row101 col2\" >0.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row102_col0\" class=\"data row102 col0\" >103</td>\n",
-       "      <td id=\"T_9ea44_row102_col1\" class=\"data row102 col1\" >StandardScaler, GenericUnivariateSelect, DecisionTreeClassifier</td>\n",
-       "      <td id=\"T_9ea44_row102_col2\" class=\"data row102 col2\" >0.250</td>\n",
+       "      <td id=\"T_8ffa7_row102_col0\" class=\"data row102 col0\" >103</td>\n",
+       "      <td id=\"T_8ffa7_row102_col1\" class=\"data row102 col1\" >StandardScaler, GenericUnivariateSelect, DecisionTreeClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row102_col2\" class=\"data row102 col2\" >0.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row103_col0\" class=\"data row103 col0\" >104</td>\n",
-       "      <td id=\"T_9ea44_row103_col1\" class=\"data row103 col1\" >RobustScaler, GenericUnivariateSelect, DecisionTreeClassifier</td>\n",
-       "      <td id=\"T_9ea44_row103_col2\" class=\"data row103 col2\" >0.250</td>\n",
+       "      <td id=\"T_8ffa7_row103_col0\" class=\"data row103 col0\" >104</td>\n",
+       "      <td id=\"T_8ffa7_row103_col1\" class=\"data row103 col1\" >RobustScaler, GenericUnivariateSelect, DecisionTreeClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row103_col2\" class=\"data row103 col2\" >0.250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row104_col0\" class=\"data row104 col0\" >105</td>\n",
-       "      <td id=\"T_9ea44_row104_col1\" class=\"data row104 col1\" >GenericUnivariateSelect, BaggingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row104_col2\" class=\"data row104 col2\" >0.236</td>\n",
+       "      <td id=\"T_8ffa7_row104_col0\" class=\"data row104 col0\" >105</td>\n",
+       "      <td id=\"T_8ffa7_row104_col1\" class=\"data row104 col1\" >GenericUnivariateSelect, BaggingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row104_col2\" class=\"data row104 col2\" >0.236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row105_col0\" class=\"data row105 col0\" >106</td>\n",
-       "      <td id=\"T_9ea44_row105_col1\" class=\"data row105 col1\" >MaxAbsScaler, SelectPercentile, SGDClassifier</td>\n",
-       "      <td id=\"T_9ea44_row105_col2\" class=\"data row105 col2\" >0.222</td>\n",
+       "      <td id=\"T_8ffa7_row105_col0\" class=\"data row105 col0\" >106</td>\n",
+       "      <td id=\"T_8ffa7_row105_col1\" class=\"data row105 col1\" >MaxAbsScaler, SelectPercentile, SGDClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row105_col2\" class=\"data row105 col2\" >0.222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row106_col0\" class=\"data row106 col0\" >107</td>\n",
-       "      <td id=\"T_9ea44_row106_col1\" class=\"data row106 col1\" >GenericUnivariateSelect, RandomForestClassifier</td>\n",
-       "      <td id=\"T_9ea44_row106_col2\" class=\"data row106 col2\" >0.222</td>\n",
+       "      <td id=\"T_8ffa7_row106_col0\" class=\"data row106 col0\" >107</td>\n",
+       "      <td id=\"T_8ffa7_row106_col1\" class=\"data row106 col1\" >GenericUnivariateSelect, RandomForestClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row106_col2\" class=\"data row106 col2\" >0.222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row107_col0\" class=\"data row107 col0\" >108</td>\n",
-       "      <td id=\"T_9ea44_row107_col1\" class=\"data row107 col1\" >MaxAbsScaler, GenericUnivariateSelect, LGBMClassifier</td>\n",
-       "      <td id=\"T_9ea44_row107_col2\" class=\"data row107 col2\" >0.208</td>\n",
+       "      <td id=\"T_8ffa7_row107_col0\" class=\"data row107 col0\" >108</td>\n",
+       "      <td id=\"T_8ffa7_row107_col1\" class=\"data row107 col1\" >MaxAbsScaler, GenericUnivariateSelect, LGBMClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row107_col2\" class=\"data row107 col2\" >0.208</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row108_col0\" class=\"data row108 col0\" >109</td>\n",
-       "      <td id=\"T_9ea44_row108_col1\" class=\"data row108 col1\" >MaxAbsScaler, GenericUnivariateSelect, BaggingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row108_col2\" class=\"data row108 col2\" >0.208</td>\n",
+       "      <td id=\"T_8ffa7_row108_col0\" class=\"data row108 col0\" >109</td>\n",
+       "      <td id=\"T_8ffa7_row108_col1\" class=\"data row108 col1\" >MaxAbsScaler, GenericUnivariateSelect, BaggingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row108_col2\" class=\"data row108 col2\" >0.208</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row109_col0\" class=\"data row109 col0\" >110</td>\n",
-       "      <td id=\"T_9ea44_row109_col1\" class=\"data row109 col1\" >GenericUnivariateSelect, LGBMClassifier</td>\n",
-       "      <td id=\"T_9ea44_row109_col2\" class=\"data row109 col2\" >0.208</td>\n",
+       "      <td id=\"T_8ffa7_row109_col0\" class=\"data row109 col0\" >110</td>\n",
+       "      <td id=\"T_8ffa7_row109_col1\" class=\"data row109 col1\" >GenericUnivariateSelect, LGBMClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row109_col2\" class=\"data row109 col2\" >0.208</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row110_col0\" class=\"data row110 col0\" >111</td>\n",
-       "      <td id=\"T_9ea44_row110_col1\" class=\"data row110 col1\" >MaxAbsScaler, GenericUnivariateSelect, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row110_col2\" class=\"data row110 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row110_col0\" class=\"data row110 col0\" >111</td>\n",
+       "      <td id=\"T_8ffa7_row110_col1\" class=\"data row110 col1\" >MaxAbsScaler, GenericUnivariateSelect, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row110_col2\" class=\"data row110 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row111_col0\" class=\"data row111 col0\" >112</td>\n",
-       "      <td id=\"T_9ea44_row111_col1\" class=\"data row111 col1\" >MaxAbsScaler, GenericUnivariateSelect, GradientBoostingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row111_col2\" class=\"data row111 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row111_col0\" class=\"data row111 col0\" >112</td>\n",
+       "      <td id=\"T_8ffa7_row111_col1\" class=\"data row111 col1\" >MaxAbsScaler, GenericUnivariateSelect, GradientBoostingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row111_col2\" class=\"data row111 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row112_col0\" class=\"data row112 col0\" >113</td>\n",
-       "      <td id=\"T_9ea44_row112_col1\" class=\"data row112 col1\" >MaxAbsScaler, GenericUnivariateSelect, QuadraticDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row112_col2\" class=\"data row112 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row112_col0\" class=\"data row112 col0\" >113</td>\n",
+       "      <td id=\"T_8ffa7_row112_col1\" class=\"data row112 col1\" >MaxAbsScaler, GenericUnivariateSelect, QuadraticDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row112_col2\" class=\"data row112 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row113_col0\" class=\"data row113 col0\" >114</td>\n",
-       "      <td id=\"T_9ea44_row113_col1\" class=\"data row113 col1\" >MaxAbsScaler, GenericUnivariateSelect, RandomForestClassifier</td>\n",
-       "      <td id=\"T_9ea44_row113_col2\" class=\"data row113 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row113_col0\" class=\"data row113 col0\" >114</td>\n",
+       "      <td id=\"T_8ffa7_row113_col1\" class=\"data row113 col1\" >MaxAbsScaler, GenericUnivariateSelect, RandomForestClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row113_col2\" class=\"data row113 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row114_col0\" class=\"data row114 col0\" >115</td>\n",
-       "      <td id=\"T_9ea44_row114_col1\" class=\"data row114 col1\" >MaxAbsScaler, GenericUnivariateSelect, XGBClassifier</td>\n",
-       "      <td id=\"T_9ea44_row114_col2\" class=\"data row114 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row114_col0\" class=\"data row114 col0\" >115</td>\n",
+       "      <td id=\"T_8ffa7_row114_col1\" class=\"data row114 col1\" >MaxAbsScaler, GenericUnivariateSelect, XGBClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row114_col2\" class=\"data row114 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row115_col0\" class=\"data row115 col0\" >116</td>\n",
-       "      <td id=\"T_9ea44_row115_col1\" class=\"data row115 col1\" >GenericUnivariateSelect, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row115_col2\" class=\"data row115 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row115_col0\" class=\"data row115 col0\" >116</td>\n",
+       "      <td id=\"T_8ffa7_row115_col1\" class=\"data row115 col1\" >GenericUnivariateSelect, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row115_col2\" class=\"data row115 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row116_col0\" class=\"data row116 col0\" >117</td>\n",
-       "      <td id=\"T_9ea44_row116_col1\" class=\"data row116 col1\" >StandardScaler, GenericUnivariateSelect, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row116_col2\" class=\"data row116 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row116_col0\" class=\"data row116 col0\" >117</td>\n",
+       "      <td id=\"T_8ffa7_row116_col1\" class=\"data row116 col1\" >StandardScaler, GenericUnivariateSelect, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row116_col2\" class=\"data row116 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row117_col0\" class=\"data row117 col0\" >118</td>\n",
-       "      <td id=\"T_9ea44_row117_col1\" class=\"data row117 col1\" >RobustScaler, GenericUnivariateSelect, GaussianNB</td>\n",
-       "      <td id=\"T_9ea44_row117_col2\" class=\"data row117 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row117_col0\" class=\"data row117 col0\" >118</td>\n",
+       "      <td id=\"T_8ffa7_row117_col1\" class=\"data row117 col1\" >RobustScaler, GenericUnivariateSelect, GaussianNB</td>\n",
+       "      <td id=\"T_8ffa7_row117_col2\" class=\"data row117 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row118_col0\" class=\"data row118 col0\" >119</td>\n",
-       "      <td id=\"T_9ea44_row118_col1\" class=\"data row118 col1\" >GenericUnivariateSelect, GradientBoostingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row118_col2\" class=\"data row118 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row118_col0\" class=\"data row118 col0\" >119</td>\n",
+       "      <td id=\"T_8ffa7_row118_col1\" class=\"data row118 col1\" >GenericUnivariateSelect, GradientBoostingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row118_col2\" class=\"data row118 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row119_col0\" class=\"data row119 col0\" >120</td>\n",
-       "      <td id=\"T_9ea44_row119_col1\" class=\"data row119 col1\" >StandardScaler, GenericUnivariateSelect, GradientBoostingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row119_col2\" class=\"data row119 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row119_col0\" class=\"data row119 col0\" >120</td>\n",
+       "      <td id=\"T_8ffa7_row119_col1\" class=\"data row119 col1\" >StandardScaler, GenericUnivariateSelect, GradientBoostingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row119_col2\" class=\"data row119 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row120_col0\" class=\"data row120 col0\" >121</td>\n",
-       "      <td id=\"T_9ea44_row120_col1\" class=\"data row120 col1\" >RobustScaler, GenericUnivariateSelect, GradientBoostingClassifier</td>\n",
-       "      <td id=\"T_9ea44_row120_col2\" class=\"data row120 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row120_col0\" class=\"data row120 col0\" >121</td>\n",
+       "      <td id=\"T_8ffa7_row120_col1\" class=\"data row120 col1\" >RobustScaler, GenericUnivariateSelect, GradientBoostingClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row120_col2\" class=\"data row120 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row121_col0\" class=\"data row121 col0\" >122</td>\n",
-       "      <td id=\"T_9ea44_row121_col1\" class=\"data row121 col1\" >GenericUnivariateSelect, QuadraticDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row121_col2\" class=\"data row121 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row121_col0\" class=\"data row121 col0\" >122</td>\n",
+       "      <td id=\"T_8ffa7_row121_col1\" class=\"data row121 col1\" >GenericUnivariateSelect, QuadraticDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row121_col2\" class=\"data row121 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row122_col0\" class=\"data row122 col0\" >123</td>\n",
-       "      <td id=\"T_9ea44_row122_col1\" class=\"data row122 col1\" >GenericUnivariateSelect, XGBClassifier</td>\n",
-       "      <td id=\"T_9ea44_row122_col2\" class=\"data row122 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row122_col0\" class=\"data row122 col0\" >123</td>\n",
+       "      <td id=\"T_8ffa7_row122_col1\" class=\"data row122 col1\" >GenericUnivariateSelect, XGBClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row122_col2\" class=\"data row122 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row123_col0\" class=\"data row123 col0\" >124</td>\n",
-       "      <td id=\"T_9ea44_row123_col1\" class=\"data row123 col1\" >StandardScaler, GenericUnivariateSelect, QuadraticDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row123_col2\" class=\"data row123 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row123_col0\" class=\"data row123 col0\" >124</td>\n",
+       "      <td id=\"T_8ffa7_row123_col1\" class=\"data row123 col1\" >StandardScaler, GenericUnivariateSelect, QuadraticDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row123_col2\" class=\"data row123 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row124_col0\" class=\"data row124 col0\" >125</td>\n",
-       "      <td id=\"T_9ea44_row124_col1\" class=\"data row124 col1\" >RobustScaler, GenericUnivariateSelect, RandomForestClassifier</td>\n",
-       "      <td id=\"T_9ea44_row124_col2\" class=\"data row124 col2\" >0.194</td>\n",
+       "      <td id=\"T_8ffa7_row124_col0\" class=\"data row124 col0\" >125</td>\n",
+       "      <td id=\"T_8ffa7_row124_col1\" class=\"data row124 col1\" >RobustScaler, GenericUnivariateSelect, RandomForestClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row124_col2\" class=\"data row124 col2\" >0.194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row125_col0\" class=\"data row125 col0\" >126</td>\n",
-       "      <td id=\"T_9ea44_row125_col1\" class=\"data row125 col1\" >MaxAbsScaler, GenericUnivariateSelect, KNeighborsClassifier</td>\n",
-       "      <td id=\"T_9ea44_row125_col2\" class=\"data row125 col2\" >0.181</td>\n",
+       "      <td id=\"T_8ffa7_row125_col0\" class=\"data row125 col0\" >126</td>\n",
+       "      <td id=\"T_8ffa7_row125_col1\" class=\"data row125 col1\" >MaxAbsScaler, GenericUnivariateSelect, KNeighborsClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row125_col2\" class=\"data row125 col2\" >0.181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row126_col0\" class=\"data row126 col0\" >127</td>\n",
-       "      <td id=\"T_9ea44_row126_col1\" class=\"data row126 col1\" >MaxAbsScaler, GenericUnivariateSelect, SGDClassifier</td>\n",
-       "      <td id=\"T_9ea44_row126_col2\" class=\"data row126 col2\" >0.181</td>\n",
+       "      <td id=\"T_8ffa7_row126_col0\" class=\"data row126 col0\" >127</td>\n",
+       "      <td id=\"T_8ffa7_row126_col1\" class=\"data row126 col1\" >MaxAbsScaler, GenericUnivariateSelect, SGDClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row126_col2\" class=\"data row126 col2\" >0.181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row127_col0\" class=\"data row127 col0\" >128</td>\n",
-       "      <td id=\"T_9ea44_row127_col1\" class=\"data row127 col1\" >MaxAbsScaler, GenericUnivariateSelect, SVC</td>\n",
-       "      <td id=\"T_9ea44_row127_col2\" class=\"data row127 col2\" >0.181</td>\n",
+       "      <td id=\"T_8ffa7_row127_col0\" class=\"data row127 col0\" >128</td>\n",
+       "      <td id=\"T_8ffa7_row127_col1\" class=\"data row127 col1\" >MaxAbsScaler, GenericUnivariateSelect, SVC</td>\n",
+       "      <td id=\"T_8ffa7_row127_col2\" class=\"data row127 col2\" >0.181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row128_col0\" class=\"data row128 col0\" >129</td>\n",
-       "      <td id=\"T_9ea44_row128_col1\" class=\"data row128 col1\" >GenericUnivariateSelect, KNeighborsClassifier</td>\n",
-       "      <td id=\"T_9ea44_row128_col2\" class=\"data row128 col2\" >0.181</td>\n",
+       "      <td id=\"T_8ffa7_row128_col0\" class=\"data row128 col0\" >129</td>\n",
+       "      <td id=\"T_8ffa7_row128_col1\" class=\"data row128 col1\" >GenericUnivariateSelect, KNeighborsClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row128_col2\" class=\"data row128 col2\" >0.181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row129_col0\" class=\"data row129 col0\" >130</td>\n",
-       "      <td id=\"T_9ea44_row129_col1\" class=\"data row129 col1\" >StandardScaler, GenericUnivariateSelect, KNeighborsClassifier</td>\n",
-       "      <td id=\"T_9ea44_row129_col2\" class=\"data row129 col2\" >0.181</td>\n",
+       "      <td id=\"T_8ffa7_row129_col0\" class=\"data row129 col0\" >130</td>\n",
+       "      <td id=\"T_8ffa7_row129_col1\" class=\"data row129 col1\" >StandardScaler, GenericUnivariateSelect, KNeighborsClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row129_col2\" class=\"data row129 col2\" >0.181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row130_col0\" class=\"data row130 col0\" >131</td>\n",
-       "      <td id=\"T_9ea44_row130_col1\" class=\"data row130 col1\" >RobustScaler, GenericUnivariateSelect, KNeighborsClassifier</td>\n",
-       "      <td id=\"T_9ea44_row130_col2\" class=\"data row130 col2\" >0.181</td>\n",
+       "      <td id=\"T_8ffa7_row130_col0\" class=\"data row130 col0\" >131</td>\n",
+       "      <td id=\"T_8ffa7_row130_col1\" class=\"data row130 col1\" >RobustScaler, GenericUnivariateSelect, KNeighborsClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row130_col2\" class=\"data row130 col2\" >0.181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row131_col0\" class=\"data row131 col0\" >132</td>\n",
-       "      <td id=\"T_9ea44_row131_col1\" class=\"data row131 col1\" >GenericUnivariateSelect, SVC</td>\n",
-       "      <td id=\"T_9ea44_row131_col2\" class=\"data row131 col2\" >0.181</td>\n",
+       "      <td id=\"T_8ffa7_row131_col0\" class=\"data row131 col0\" >132</td>\n",
+       "      <td id=\"T_8ffa7_row131_col1\" class=\"data row131 col1\" >GenericUnivariateSelect, SVC</td>\n",
+       "      <td id=\"T_8ffa7_row131_col2\" class=\"data row131 col2\" >0.181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row132_col0\" class=\"data row132 col0\" >133</td>\n",
-       "      <td id=\"T_9ea44_row132_col1\" class=\"data row132 col1\" >StandardScaler, GenericUnivariateSelect, SVC</td>\n",
-       "      <td id=\"T_9ea44_row132_col2\" class=\"data row132 col2\" >0.181</td>\n",
+       "      <td id=\"T_8ffa7_row132_col0\" class=\"data row132 col0\" >133</td>\n",
+       "      <td id=\"T_8ffa7_row132_col1\" class=\"data row132 col1\" >StandardScaler, GenericUnivariateSelect, SVC</td>\n",
+       "      <td id=\"T_8ffa7_row132_col2\" class=\"data row132 col2\" >0.181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row133_col0\" class=\"data row133 col0\" >134</td>\n",
-       "      <td id=\"T_9ea44_row133_col1\" class=\"data row133 col1\" >StandardScaler, GenericUnivariateSelect, RandomForestClassifier</td>\n",
-       "      <td id=\"T_9ea44_row133_col2\" class=\"data row133 col2\" >0.181</td>\n",
+       "      <td id=\"T_8ffa7_row133_col0\" class=\"data row133 col0\" >134</td>\n",
+       "      <td id=\"T_8ffa7_row133_col1\" class=\"data row133 col1\" >StandardScaler, GenericUnivariateSelect, RandomForestClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row133_col2\" class=\"data row133 col2\" >0.181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row134_col0\" class=\"data row134 col0\" >135</td>\n",
-       "      <td id=\"T_9ea44_row134_col1\" class=\"data row134 col1\" >StandardScaler, GenericUnivariateSelect, LogisticRegression</td>\n",
-       "      <td id=\"T_9ea44_row134_col2\" class=\"data row134 col2\" >0.153</td>\n",
+       "      <td id=\"T_8ffa7_row134_col0\" class=\"data row134 col0\" >135</td>\n",
+       "      <td id=\"T_8ffa7_row134_col1\" class=\"data row134 col1\" >StandardScaler, GenericUnivariateSelect, LogisticRegression</td>\n",
+       "      <td id=\"T_8ffa7_row134_col2\" class=\"data row134 col2\" >0.153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row135_col0\" class=\"data row135 col0\" >136</td>\n",
-       "      <td id=\"T_9ea44_row135_col1\" class=\"data row135 col1\" >RobustScaler, GenericUnivariateSelect, LogisticRegression</td>\n",
-       "      <td id=\"T_9ea44_row135_col2\" class=\"data row135 col2\" >0.153</td>\n",
+       "      <td id=\"T_8ffa7_row135_col0\" class=\"data row135 col0\" >136</td>\n",
+       "      <td id=\"T_8ffa7_row135_col1\" class=\"data row135 col1\" >RobustScaler, GenericUnivariateSelect, LogisticRegression</td>\n",
+       "      <td id=\"T_8ffa7_row135_col2\" class=\"data row135 col2\" >0.153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row136_col0\" class=\"data row136 col0\" >137</td>\n",
-       "      <td id=\"T_9ea44_row136_col1\" class=\"data row136 col1\" >MaxAbsScaler, GenericUnivariateSelect, LinearSVC</td>\n",
-       "      <td id=\"T_9ea44_row136_col2\" class=\"data row136 col2\" >0.139</td>\n",
+       "      <td id=\"T_8ffa7_row136_col0\" class=\"data row136 col0\" >137</td>\n",
+       "      <td id=\"T_8ffa7_row136_col1\" class=\"data row136 col1\" >MaxAbsScaler, GenericUnivariateSelect, LinearSVC</td>\n",
+       "      <td id=\"T_8ffa7_row136_col2\" class=\"data row136 col2\" >0.139</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row137_col0\" class=\"data row137 col0\" >138</td>\n",
-       "      <td id=\"T_9ea44_row137_col1\" class=\"data row137 col1\" >GenericUnivariateSelect, LinearSVC</td>\n",
-       "      <td id=\"T_9ea44_row137_col2\" class=\"data row137 col2\" >0.139</td>\n",
+       "      <td id=\"T_8ffa7_row137_col0\" class=\"data row137 col0\" >138</td>\n",
+       "      <td id=\"T_8ffa7_row137_col1\" class=\"data row137 col1\" >GenericUnivariateSelect, LinearSVC</td>\n",
+       "      <td id=\"T_8ffa7_row137_col2\" class=\"data row137 col2\" >0.139</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row138_col0\" class=\"data row138 col0\" >139</td>\n",
-       "      <td id=\"T_9ea44_row138_col1\" class=\"data row138 col1\" >MaxAbsScaler, SelectKBest, SGDClassifier</td>\n",
-       "      <td id=\"T_9ea44_row138_col2\" class=\"data row138 col2\" >0.139</td>\n",
+       "      <td id=\"T_8ffa7_row138_col0\" class=\"data row138 col0\" >139</td>\n",
+       "      <td id=\"T_8ffa7_row138_col1\" class=\"data row138 col1\" >MaxAbsScaler, SelectKBest, SGDClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row138_col2\" class=\"data row138 col2\" >0.139</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row139_col0\" class=\"data row139 col0\" >140</td>\n",
-       "      <td id=\"T_9ea44_row139_col1\" class=\"data row139 col1\" >StandardScaler, GenericUnivariateSelect, LinearSVC</td>\n",
-       "      <td id=\"T_9ea44_row139_col2\" class=\"data row139 col2\" >0.139</td>\n",
+       "      <td id=\"T_8ffa7_row139_col0\" class=\"data row139 col0\" >140</td>\n",
+       "      <td id=\"T_8ffa7_row139_col1\" class=\"data row139 col1\" >StandardScaler, GenericUnivariateSelect, LinearSVC</td>\n",
+       "      <td id=\"T_8ffa7_row139_col2\" class=\"data row139 col2\" >0.139</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row140_col0\" class=\"data row140 col0\" >141</td>\n",
-       "      <td id=\"T_9ea44_row140_col1\" class=\"data row140 col1\" >RobustScaler, GenericUnivariateSelect, LinearSVC</td>\n",
-       "      <td id=\"T_9ea44_row140_col2\" class=\"data row140 col2\" >0.139</td>\n",
+       "      <td id=\"T_8ffa7_row140_col0\" class=\"data row140 col0\" >141</td>\n",
+       "      <td id=\"T_8ffa7_row140_col1\" class=\"data row140 col1\" >RobustScaler, GenericUnivariateSelect, LinearSVC</td>\n",
+       "      <td id=\"T_8ffa7_row140_col2\" class=\"data row140 col2\" >0.139</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row141_col0\" class=\"data row141 col0\" >142</td>\n",
-       "      <td id=\"T_9ea44_row141_col1\" class=\"data row141 col1\" >MaxAbsScaler, GenericUnivariateSelect, LinearDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row141_col2\" class=\"data row141 col2\" >0.125</td>\n",
+       "      <td id=\"T_8ffa7_row141_col0\" class=\"data row141 col0\" >142</td>\n",
+       "      <td id=\"T_8ffa7_row141_col1\" class=\"data row141 col1\" >MaxAbsScaler, GenericUnivariateSelect, LinearDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row141_col2\" class=\"data row141 col2\" >0.125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row142_col0\" class=\"data row142 col0\" >143</td>\n",
-       "      <td id=\"T_9ea44_row142_col1\" class=\"data row142 col1\" >MaxAbsScaler, GenericUnivariateSelect, PassiveAggressiveClassifier</td>\n",
-       "      <td id=\"T_9ea44_row142_col2\" class=\"data row142 col2\" >0.125</td>\n",
+       "      <td id=\"T_8ffa7_row142_col0\" class=\"data row142 col0\" >143</td>\n",
+       "      <td id=\"T_8ffa7_row142_col1\" class=\"data row142 col1\" >MaxAbsScaler, GenericUnivariateSelect, PassiveAggressiveClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row142_col2\" class=\"data row142 col2\" >0.125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row143_col0\" class=\"data row143 col0\" >144</td>\n",
-       "      <td id=\"T_9ea44_row143_col1\" class=\"data row143 col1\" >GenericUnivariateSelect, LinearDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row143_col2\" class=\"data row143 col2\" >0.125</td>\n",
+       "      <td id=\"T_8ffa7_row143_col0\" class=\"data row143 col0\" >144</td>\n",
+       "      <td id=\"T_8ffa7_row143_col1\" class=\"data row143 col1\" >GenericUnivariateSelect, LinearDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row143_col2\" class=\"data row143 col2\" >0.125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row144_col0\" class=\"data row144 col0\" >145</td>\n",
-       "      <td id=\"T_9ea44_row144_col1\" class=\"data row144 col1\" >StandardScaler, GenericUnivariateSelect, LinearDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row144_col2\" class=\"data row144 col2\" >0.125</td>\n",
+       "      <td id=\"T_8ffa7_row144_col0\" class=\"data row144 col0\" >145</td>\n",
+       "      <td id=\"T_8ffa7_row144_col1\" class=\"data row144 col1\" >StandardScaler, GenericUnivariateSelect, LinearDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row144_col2\" class=\"data row144 col2\" >0.125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row145_col0\" class=\"data row145 col0\" >146</td>\n",
-       "      <td id=\"T_9ea44_row145_col1\" class=\"data row145 col1\" >RobustScaler, GenericUnivariateSelect, LinearDiscriminantAnalysis</td>\n",
-       "      <td id=\"T_9ea44_row145_col2\" class=\"data row145 col2\" >0.125</td>\n",
+       "      <td id=\"T_8ffa7_row145_col0\" class=\"data row145 col0\" >146</td>\n",
+       "      <td id=\"T_8ffa7_row145_col1\" class=\"data row145 col1\" >RobustScaler, GenericUnivariateSelect, LinearDiscriminantAnalysis</td>\n",
+       "      <td id=\"T_8ffa7_row145_col2\" class=\"data row145 col2\" >0.125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row146_col0\" class=\"data row146 col0\" >147</td>\n",
-       "      <td id=\"T_9ea44_row146_col1\" class=\"data row146 col1\" >MaxAbsScaler, SelectPercentile, PassiveAggressiveClassifier</td>\n",
-       "      <td id=\"T_9ea44_row146_col2\" class=\"data row146 col2\" >0.125</td>\n",
+       "      <td id=\"T_8ffa7_row146_col0\" class=\"data row146 col0\" >147</td>\n",
+       "      <td id=\"T_8ffa7_row146_col1\" class=\"data row146 col1\" >MaxAbsScaler, SelectPercentile, PassiveAggressiveClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row146_col2\" class=\"data row146 col2\" >0.125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row147_col0\" class=\"data row147 col0\" >148</td>\n",
-       "      <td id=\"T_9ea44_row147_col1\" class=\"data row147 col1\" >GenericUnivariateSelect, SGDClassifier</td>\n",
-       "      <td id=\"T_9ea44_row147_col2\" class=\"data row147 col2\" >0.125</td>\n",
+       "      <td id=\"T_8ffa7_row147_col0\" class=\"data row147 col0\" >148</td>\n",
+       "      <td id=\"T_8ffa7_row147_col1\" class=\"data row147 col1\" >GenericUnivariateSelect, SGDClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row147_col2\" class=\"data row147 col2\" >0.125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row148_col0\" class=\"data row148 col0\" >149</td>\n",
-       "      <td id=\"T_9ea44_row148_col1\" class=\"data row148 col1\" >MaxAbsScaler, GenericUnivariateSelect, LogisticRegression</td>\n",
-       "      <td id=\"T_9ea44_row148_col2\" class=\"data row148 col2\" >0.097</td>\n",
+       "      <td id=\"T_8ffa7_row148_col0\" class=\"data row148 col0\" >149</td>\n",
+       "      <td id=\"T_8ffa7_row148_col1\" class=\"data row148 col1\" >MaxAbsScaler, GenericUnivariateSelect, LogisticRegression</td>\n",
+       "      <td id=\"T_8ffa7_row148_col2\" class=\"data row148 col2\" >0.097</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row149_col0\" class=\"data row149 col0\" >150</td>\n",
-       "      <td id=\"T_9ea44_row149_col1\" class=\"data row149 col1\" >MaxAbsScaler, SelectKBest, PassiveAggressiveClassifier</td>\n",
-       "      <td id=\"T_9ea44_row149_col2\" class=\"data row149 col2\" >0.097</td>\n",
+       "      <td id=\"T_8ffa7_row149_col0\" class=\"data row149 col0\" >150</td>\n",
+       "      <td id=\"T_8ffa7_row149_col1\" class=\"data row149 col1\" >MaxAbsScaler, SelectKBest, PassiveAggressiveClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row149_col2\" class=\"data row149 col2\" >0.097</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row150_col0\" class=\"data row150 col0\" >151</td>\n",
-       "      <td id=\"T_9ea44_row150_col1\" class=\"data row150 col1\" >GenericUnivariateSelect, LogisticRegression</td>\n",
-       "      <td id=\"T_9ea44_row150_col2\" class=\"data row150 col2\" >0.097</td>\n",
+       "      <td id=\"T_8ffa7_row150_col0\" class=\"data row150 col0\" >151</td>\n",
+       "      <td id=\"T_8ffa7_row150_col1\" class=\"data row150 col1\" >GenericUnivariateSelect, LogisticRegression</td>\n",
+       "      <td id=\"T_8ffa7_row150_col2\" class=\"data row150 col2\" >0.097</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row151_col0\" class=\"data row151 col0\" >152</td>\n",
-       "      <td id=\"T_9ea44_row151_col1\" class=\"data row151 col1\" >StandardScaler, GenericUnivariateSelect, SGDClassifier</td>\n",
-       "      <td id=\"T_9ea44_row151_col2\" class=\"data row151 col2\" >0.097</td>\n",
+       "      <td id=\"T_8ffa7_row151_col0\" class=\"data row151 col0\" >152</td>\n",
+       "      <td id=\"T_8ffa7_row151_col1\" class=\"data row151 col1\" >StandardScaler, GenericUnivariateSelect, SGDClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row151_col2\" class=\"data row151 col2\" >0.097</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row152_col0\" class=\"data row152 col0\" >153</td>\n",
-       "      <td id=\"T_9ea44_row152_col1\" class=\"data row152 col1\" >RobustScaler, GenericUnivariateSelect, SGDClassifier</td>\n",
-       "      <td id=\"T_9ea44_row152_col2\" class=\"data row152 col2\" >0.097</td>\n",
+       "      <td id=\"T_8ffa7_row152_col0\" class=\"data row152 col0\" >153</td>\n",
+       "      <td id=\"T_8ffa7_row152_col1\" class=\"data row152 col1\" >RobustScaler, GenericUnivariateSelect, SGDClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row152_col2\" class=\"data row152 col2\" >0.097</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row153_col0\" class=\"data row153 col0\" >154</td>\n",
-       "      <td id=\"T_9ea44_row153_col1\" class=\"data row153 col1\" >MaxAbsScaler, SelectKBest, MultinomialNB</td>\n",
-       "      <td id=\"T_9ea44_row153_col2\" class=\"data row153 col2\" >0.083</td>\n",
+       "      <td id=\"T_8ffa7_row153_col0\" class=\"data row153 col0\" >154</td>\n",
+       "      <td id=\"T_8ffa7_row153_col1\" class=\"data row153 col1\" >MaxAbsScaler, SelectKBest, MultinomialNB</td>\n",
+       "      <td id=\"T_8ffa7_row153_col2\" class=\"data row153 col2\" >0.083</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row154_col0\" class=\"data row154 col0\" >155</td>\n",
-       "      <td id=\"T_9ea44_row154_col1\" class=\"data row154 col1\" >GenericUnivariateSelect, PassiveAggressiveClassifier</td>\n",
-       "      <td id=\"T_9ea44_row154_col2\" class=\"data row154 col2\" >0.083</td>\n",
+       "      <td id=\"T_8ffa7_row154_col0\" class=\"data row154 col0\" >155</td>\n",
+       "      <td id=\"T_8ffa7_row154_col1\" class=\"data row154 col1\" >GenericUnivariateSelect, PassiveAggressiveClassifier</td>\n",
+       "      <td id=\"T_8ffa7_row154_col2\" class=\"data row154 col2\" >0.083</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row155_col0\" class=\"data row155 col0\" >156</td>\n",
-       "      <td id=\"T_9ea44_row155_col1\" class=\"data row155 col1\" >MaxAbsScaler, SelectPercentile, MultinomialNB</td>\n",
-       "      <td id=\"T_9ea44_row155_col2\" class=\"data row155 col2\" >0.069</td>\n",
+       "      <td id=\"T_8ffa7_row155_col0\" class=\"data row155 col0\" >156</td>\n",
+       "      <td id=\"T_8ffa7_row155_col1\" class=\"data row155 col1\" >MaxAbsScaler, SelectPercentile, MultinomialNB</td>\n",
+       "      <td id=\"T_8ffa7_row155_col2\" class=\"data row155 col2\" >0.069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row156_col0\" class=\"data row156 col0\" >157</td>\n",
-       "      <td id=\"T_9ea44_row156_col1\" class=\"data row156 col1\" >StandardScaler, GenericUnivariateSelect, BernoulliNB</td>\n",
-       "      <td id=\"T_9ea44_row156_col2\" class=\"data row156 col2\" >0.069</td>\n",
+       "      <td id=\"T_8ffa7_row156_col0\" class=\"data row156 col0\" >157</td>\n",
+       "      <td id=\"T_8ffa7_row156_col1\" class=\"data row156 col1\" >StandardScaler, GenericUnivariateSelect, BernoulliNB</td>\n",
+       "      <td id=\"T_8ffa7_row156_col2\" class=\"data row156 col2\" >0.069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row157_col0\" class=\"data row157 col0\" >158</td>\n",
-       "      <td id=\"T_9ea44_row157_col1\" class=\"data row157 col1\" >RobustScaler, GenericUnivariateSelect, BernoulliNB</td>\n",
-       "      <td id=\"T_9ea44_row157_col2\" class=\"data row157 col2\" >0.069</td>\n",
+       "      <td id=\"T_8ffa7_row157_col0\" class=\"data row157 col0\" >158</td>\n",
+       "      <td id=\"T_8ffa7_row157_col1\" class=\"data row157 col1\" >RobustScaler, GenericUnivariateSelect, BernoulliNB</td>\n",
+       "      <td id=\"T_8ffa7_row157_col2\" class=\"data row157 col2\" >0.069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row158_col0\" class=\"data row158 col0\" >159</td>\n",
-       "      <td id=\"T_9ea44_row158_col1\" class=\"data row158 col1\" >MaxAbsScaler, GenericUnivariateSelect, BernoulliNB</td>\n",
-       "      <td id=\"T_9ea44_row158_col2\" class=\"data row158 col2\" >0.028</td>\n",
+       "      <td id=\"T_8ffa7_row158_col0\" class=\"data row158 col0\" >159</td>\n",
+       "      <td id=\"T_8ffa7_row158_col1\" class=\"data row158 col1\" >MaxAbsScaler, GenericUnivariateSelect, BernoulliNB</td>\n",
+       "      <td id=\"T_8ffa7_row158_col2\" class=\"data row158 col2\" >0.028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row159_col0\" class=\"data row159 col0\" >160</td>\n",
-       "      <td id=\"T_9ea44_row159_col1\" class=\"data row159 col1\" >MaxAbsScaler, GenericUnivariateSelect, MultinomialNB</td>\n",
-       "      <td id=\"T_9ea44_row159_col2\" class=\"data row159 col2\" >0.028</td>\n",
+       "      <td id=\"T_8ffa7_row159_col0\" class=\"data row159 col0\" >160</td>\n",
+       "      <td id=\"T_8ffa7_row159_col1\" class=\"data row159 col1\" >MaxAbsScaler, GenericUnivariateSelect, MultinomialNB</td>\n",
+       "      <td id=\"T_8ffa7_row159_col2\" class=\"data row159 col2\" >0.028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row160_col0\" class=\"data row160 col0\" >161</td>\n",
-       "      <td id=\"T_9ea44_row160_col1\" class=\"data row160 col1\" >MaxAbsScaler, BernoulliNB</td>\n",
-       "      <td id=\"T_9ea44_row160_col2\" class=\"data row160 col2\" >0.028</td>\n",
+       "      <td id=\"T_8ffa7_row160_col0\" class=\"data row160 col0\" >161</td>\n",
+       "      <td id=\"T_8ffa7_row160_col1\" class=\"data row160 col1\" >MaxAbsScaler, BernoulliNB</td>\n",
+       "      <td id=\"T_8ffa7_row160_col2\" class=\"data row160 col2\" >0.028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row161_col0\" class=\"data row161 col0\" >162</td>\n",
-       "      <td id=\"T_9ea44_row161_col1\" class=\"data row161 col1\" >MaxAbsScaler, SelectPercentile, BernoulliNB</td>\n",
-       "      <td id=\"T_9ea44_row161_col2\" class=\"data row161 col2\" >0.028</td>\n",
+       "      <td id=\"T_8ffa7_row161_col0\" class=\"data row161 col0\" >162</td>\n",
+       "      <td id=\"T_8ffa7_row161_col1\" class=\"data row161 col1\" >MaxAbsScaler, SelectPercentile, BernoulliNB</td>\n",
+       "      <td id=\"T_8ffa7_row161_col2\" class=\"data row161 col2\" >0.028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row162_col0\" class=\"data row162 col0\" >163</td>\n",
-       "      <td id=\"T_9ea44_row162_col1\" class=\"data row162 col1\" >BernoulliNB</td>\n",
-       "      <td id=\"T_9ea44_row162_col2\" class=\"data row162 col2\" >0.028</td>\n",
+       "      <td id=\"T_8ffa7_row162_col0\" class=\"data row162 col0\" >163</td>\n",
+       "      <td id=\"T_8ffa7_row162_col1\" class=\"data row162 col1\" >BernoulliNB</td>\n",
+       "      <td id=\"T_8ffa7_row162_col2\" class=\"data row162 col2\" >0.028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row163_col0\" class=\"data row163 col0\" >164</td>\n",
-       "      <td id=\"T_9ea44_row163_col1\" class=\"data row163 col1\" >GenericUnivariateSelect, BernoulliNB</td>\n",
-       "      <td id=\"T_9ea44_row163_col2\" class=\"data row163 col2\" >0.028</td>\n",
+       "      <td id=\"T_8ffa7_row163_col0\" class=\"data row163 col0\" >164</td>\n",
+       "      <td id=\"T_8ffa7_row163_col1\" class=\"data row163 col1\" >GenericUnivariateSelect, BernoulliNB</td>\n",
+       "      <td id=\"T_8ffa7_row163_col2\" class=\"data row163 col2\" >0.028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row164_col0\" class=\"data row164 col0\" >165</td>\n",
-       "      <td id=\"T_9ea44_row164_col1\" class=\"data row164 col1\" >MaxAbsScaler, SelectKBest, BernoulliNB</td>\n",
-       "      <td id=\"T_9ea44_row164_col2\" class=\"data row164 col2\" >0.028</td>\n",
+       "      <td id=\"T_8ffa7_row164_col0\" class=\"data row164 col0\" >165</td>\n",
+       "      <td id=\"T_8ffa7_row164_col1\" class=\"data row164 col1\" >MaxAbsScaler, SelectKBest, BernoulliNB</td>\n",
+       "      <td id=\"T_8ffa7_row164_col2\" class=\"data row164 col2\" >0.028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_9ea44_row165_col0\" class=\"data row165 col0\" >166</td>\n",
-       "      <td id=\"T_9ea44_row165_col1\" class=\"data row165 col1\" >GenericUnivariateSelect, MultinomialNB</td>\n",
-       "      <td id=\"T_9ea44_row165_col2\" class=\"data row165 col2\" >0.028</td>\n",
+       "      <td id=\"T_8ffa7_row165_col0\" class=\"data row165 col0\" >166</td>\n",
+       "      <td id=\"T_8ffa7_row165_col1\" class=\"data row165 col1\" >GenericUnivariateSelect, MultinomialNB</td>\n",
+       "      <td id=\"T_8ffa7_row165_col2\" class=\"data row165 col2\" >0.028</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff3f06cbf40>"
+       "<pandas.io.formats.style.Styler at 0x7f9d613c4040>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2391,7 +2391,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
diff --git a/examples/tabular_classification.py b/examples/tabular_classification.py
index e2ea2844..ec317512 100644
--- a/examples/tabular_classification.py
+++ b/examples/tabular_classification.py
@@ -5,7 +5,6 @@
 if __name__ == '__main__':
     # If running it in Windows or CUDA environment, Alpha-AutoML should be used inside of "if __name__ == '__main__':"
     # Read the datasets
-    output_path = join(dirname(__file__), 'tmp/')
     train_dataset = pd.read_csv(join(dirname(__file__), 'datasets/299_libras_move/train_data.csv'))
     test_dataset = pd.read_csv(join(dirname(__file__), 'datasets/299_libras_move/test_data.csv'))
 
@@ -16,7 +15,7 @@
     y_test = test_dataset[[target_column]]
 
     # Add settings
-    automl = AutoMLClassifier(output_path, time_bound=10)
+    automl = AutoMLClassifier(time_bound=10)
 
     # Perform the search
     automl.fit(X_train, y_train)
diff --git a/examples/tabular_regression.ipynb b/examples/tabular_regression.ipynb
index 063a9ea4..0a7a27c7 100644
--- a/examples/tabular_regression.ipynb
+++ b/examples/tabular_regression.ipynb
@@ -44,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_path = 'tmp/'\n",
+    "\n",
     "train_dataset = pd.read_csv('datasets/196_autoMpg/train_data.csv')\n",
     "test_dataset = pd.read_csv('datasets/196_autoMpg/test_data.csv')"
    ]
@@ -659,7 +659,7 @@
     }
    ],
    "source": [
-    "automl = AutoMLRegressor(output_path, time_bound=10)\n",
+    "automl = AutoMLRegressor(time_bound=10)\n",
     "automl.fit(X_train, y_train)"
    ]
   },
diff --git a/examples/tabular_regression.py b/examples/tabular_regression.py
index 8ee3aa19..3d76e4a4 100644
--- a/examples/tabular_regression.py
+++ b/examples/tabular_regression.py
@@ -5,7 +5,6 @@
 if __name__ == '__main__':
     # If running it in Windows or CUDA environment, Alpha-AutoML should be used inside of "if __name__ == '__main__':"
     # Read the datasets
-    output_path = join(dirname(__file__), 'tmp/')
     train_dataset = pd.read_csv(join(dirname(__file__), 'datasets/196_autoMpg/train_data.csv'))
     test_dataset = pd.read_csv(join(dirname(__file__), 'datasets/196_autoMpg/test_data.csv'))
 
@@ -16,7 +15,7 @@
     y_test = test_dataset[[target_column]]
 
     # Add settings
-    automl = AutoMLRegressor(output_path, time_bound=10)
+    automl = AutoMLRegressor(time_bound=10)
 
     # Perform the search
     automl.fit(X_train, y_train)
diff --git a/examples/timeseries_forecasting.ipynb b/examples/timeseries_forecasting.ipynb
index 404af864..9a1ca73e 100644
--- a/examples/timeseries_forecasting.ipynb
+++ b/examples/timeseries_forecasting.ipynb
@@ -45,7 +45,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_path = 'tmp/'\n",
+    "\n",
     "train_dataset = pd.read_csv('datasets/stock_market/train_data.csv')\n",
     "test_dataset = pd.read_csv('datasets/stock_market/test_data.csv')"
    ]
@@ -2119,7 +2119,7 @@
     }
    ],
    "source": [
-    "automl = AutoMLTimeSeries(output_path, time_bound=10, date_column='Date', target_column=target_column)\n",
+    "automl = AutoMLTimeSeries(time_bound=10, date_column='Date', target_column=target_column)\n",
     "automl.fit(X_train, y_train)"
    ]
   },
diff --git a/examples/using_different_metrics.ipynb b/examples/using_different_metrics.ipynb
index ee21f65d..c0e418d2 100644
--- a/examples/using_different_metrics.ipynb
+++ b/examples/using_different_metrics.ipynb
@@ -44,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output_path = 'tmp/'\n",
+    "\n",
     "train_dataset = pd.read_csv('datasets/299_libras_move/train_data.csv')\n",
     "test_dataset = pd.read_csv('datasets/299_libras_move/test_data.csv')"
    ]
@@ -603,7 +603,7 @@
     }
    ],
    "source": [
-    "automl = AutoMLClassifier(output_path, time_bound=1, metric='accuracy_score')\n",
+    "automl = AutoMLClassifier(time_bound=1, metric='accuracy_score')\n",
     "automl.fit(X_train, y_train)"
    ]
   },
@@ -808,7 +808,7 @@
    "source": [
     "from sklearn.metrics import f1_score\n",
     "\n",
-    "automl = AutoMLClassifier(output_path, time_bound=1, metric=f1_score, metric_kwargs={'average': 'micro'})\n",
+    "automl = AutoMLClassifier(time_bound=1, metric=f1_score, metric_kwargs={'average': 'micro'})\n",
     "automl.fit(X_train, y_train)"
    ]
   },
@@ -1023,7 +1023,7 @@
     "    \n",
     "    return counter / float(len(y_true))\n",
     "    \n",
-    "automl = AutoMLClassifier(output_path, time_bound=1, metric=my_custom_metric)\n",
+    "automl = AutoMLClassifier(time_bound=1, metric=my_custom_metric)\n",
     "automl.fit(X_train, y_train)"
    ]
   },


Test patch:
diff --git a/tests/test_api.py b/tests/test_api.py
index 8a884782..4a78ab31 100644
--- a/tests/test_api.py
+++ b/tests/test_api.py
@@ -14,7 +14,6 @@ class TestAutoMLTimeSeries:
     """This is the testcases for AutoMLTimeSeries API object."""
 
     api = AutoMLTimeSeries(
-        output_folder="tmp/",
         time_bound=10,
         verbose=True,
         date_column="Date",
@@ -52,7 +51,6 @@ def test_column_parser(self):
 
     def test_pass_splitter_args(self):
         test_api = AutoMLTimeSeries(
-            output_folder="tmp/",
             date_column="Date",
             target_column="Value",
             split_strategy_kwargs={"n_splits": 3, "test_size": 20},
@@ -65,7 +63,6 @@ class TestAutoMLSemiSupervisedClassifier:
     """This is the testcases for AutoMLSemiSupervisedClassifier API object."""
 
     api = AutoMLSemiSupervisedClassifier(
-        output_folder="tmp/",
         time_bound=10,
         verbose=True,
         split_strategy_kwargs={'test_size': .1},