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README.md

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+---
+license: apache-2.0
+library_name: sklearn
+tags:
+- tabular-classification
+- baseline-trainer
+---
+
+## Baseline Model trained on model_tuning_mindalleeu83oz7r to apply classification on labels
+
+**Metrics of the best model:**
+
+accuracy           0.732672
+
+recall_macro       0.630156
+
+precision_macro    0.439732
+
+f1_macro           0.455558
+
+Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64
+
+
+
+**See model plot below:**
+
+<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=                 continuous  dirty_float  ...  free_string  useless
+temperatures          False        False  ...        False    False
+superconditions        True        False  ...        False    False
+is_megas              False        False  ...        False    False
+feature_0              True        False  ...        False    False
+feature_1              True        False  ...        False    False
+...                     ...          ...  ...          ...      ...
+feature_763            True        False  ...        False    False
+feature_764            True        False  ...        False    False
+feature_765            True        False  ...        False    False
+feature_766            True        False  ...        False    False
+feature_767            True        False  ...        False    False[771 rows x 7 columns])),(&#x27;logisticregression&#x27;,LogisticRegression(C=0.1, class_weight=&#x27;balanced&#x27;,max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=                 continuous  dirty_float  ...  free_string  useless
+temperatures          False        False  ...        False    False
+superconditions        True        False  ...        False    False
+is_megas              False        False  ...        False    False
+feature_0              True        False  ...        False    False
+feature_1              True        False  ...        False    False
+...                     ...          ...  ...          ...      ...
+feature_763            True        False  ...        False    False
+feature_764            True        False  ...        False    False
+feature_765            True        False  ...        False    False
+feature_766            True        False  ...        False    False
+feature_767            True        False  ...        False    False[771 rows x 7 columns])),(&#x27;logisticregression&#x27;,LogisticRegression(C=0.1, class_weight=&#x27;balanced&#x27;,max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types=                 continuous  dirty_float  ...  free_string  useless
+temperatures          False        False  ...        False    False
+superconditions        True        False  ...        False    False
+is_megas              False        False  ...        False    False
+feature_0              True        False  ...        False    False
+feature_1              True        False  ...        False    False
+...                     ...          ...  ...          ...      ...
+feature_763            True        False  ...        False    False
+feature_764            True        False  ...        False    False
+feature_765            True        False  ...        False    False
+feature_766            True        False  ...        False    False
+feature_767            True        False  ...        False    False[771 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight=&#x27;balanced&#x27;, max_iter=1000)</pre></div></div></div></div></div></div></div>
+
+**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
+
+**Logs of training** including the models tried in the process can be found in logs.txt

clf.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:654220cfc1cf90878cfcc59c6973482407bcae162385cece329753e7ef8c209a
+size 94061

logs.txt

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+Logging training
+Running DummyClassifier()
+accuracy: 0.894 recall_macro: 0.333 precision_macro: 0.298 f1_macro: 0.315
+=== new best DummyClassifier() (using recall_macro):
+accuracy: 0.894 recall_macro: 0.333 precision_macro: 0.298 f1_macro: 0.315
+
+Running GaussianNB()
+accuracy: 0.650 recall_macro: 0.492 precision_macro: 0.389 f1_macro: 0.375
+=== new best GaussianNB() (using recall_macro):
+accuracy: 0.650 recall_macro: 0.492 precision_macro: 0.389 f1_macro: 0.375
+
+Running MultinomialNB()
+accuracy: 0.892 recall_macro: 0.342 precision_macro: 0.443 f1_macro: 0.334
+Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
+accuracy: 0.820 recall_macro: 0.428 precision_macro: 0.320 f1_macro: 0.336
+Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
+accuracy: 0.572 recall_macro: 0.512 precision_macro: 0.387 f1_macro: 0.349
+=== new best DecisionTreeClassifier(class_weight='balanced', max_depth=5) (using recall_macro):
+accuracy: 0.572 recall_macro: 0.512 precision_macro: 0.387 f1_macro: 0.349
+
+Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
+accuracy: 0.649 recall_macro: 0.530 precision_macro: 0.391 f1_macro: 0.377
+=== new best DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) (using recall_macro):
+accuracy: 0.649 recall_macro: 0.530 precision_macro: 0.391 f1_macro: 0.377
+
+Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
+accuracy: 0.733 recall_macro: 0.630 precision_macro: 0.440 f1_macro: 0.456
+=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
+accuracy: 0.733 recall_macro: 0.630 precision_macro: 0.440 f1_macro: 0.456
+
+Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
+accuracy: 0.749 recall_macro: 0.617 precision_macro: 0.442 f1_macro: 0.462
+
+Best model:
+LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
+Best Scores:
+accuracy: 0.733 recall_macro: 0.630 precision_macro: 0.440 f1_macro: 0.456