YAML Metadata
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Model description
This is a gradient boosted regression model trained on ames housing dataset from OpenML.
Intended uses & limitations
This model is not ready to be used in production.
Training Procedure
[More Information Needed]
Hyperparameters
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Hyperparameter | Value |
---|---|
memory | |
steps | [('columntransformer', ColumnTransformer(transformers=[('simpleimputer', SimpleImputer(add_indicator=True), <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>), ('ordinalencoder', OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value', unknown_value=-1), <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)])), ('histgradientboostingregressor', HistGradientBoostingRegressor(random_state=0))] |
verbose | False |
columntransformer | ColumnTransformer(transformers=[('simpleimputer', SimpleImputer(add_indicator=True), <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>), ('ordinalencoder', OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value', unknown_value=-1), <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)]) |
histgradientboostingregressor | HistGradientBoostingRegressor(random_state=0) |
columntransformer__n_jobs | |
columntransformer__remainder | drop |
columntransformer__sparse_threshold | 0.3 |
columntransformer__transformer_weights | |
columntransformer__transformers | [('simpleimputer', SimpleImputer(add_indicator=True), <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>), ('ordinalencoder', OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value', unknown_value=-1), <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)] |
columntransformer__verbose | False |
columntransformer__verbose_feature_names_out | True |
columntransformer__simpleimputer | SimpleImputer(add_indicator=True) |
columntransformer__ordinalencoder | OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value', unknown_value=-1) |
columntransformer__simpleimputer__add_indicator | True |
columntransformer__simpleimputer__copy | True |
columntransformer__simpleimputer__fill_value | |
columntransformer__simpleimputer__keep_empty_features | False |
columntransformer__simpleimputer__missing_values | nan |
columntransformer__simpleimputer__strategy | mean |
columntransformer__simpleimputer__verbose | deprecated |
columntransformer__ordinalencoder__categories | auto |
columntransformer__ordinalencoder__dtype | <class 'numpy.float64'> |
columntransformer__ordinalencoder__encoded_missing_value | -2 |
columntransformer__ordinalencoder__handle_unknown | use_encoded_value |
columntransformer__ordinalencoder__unknown_value | -1 |
histgradientboostingregressor__categorical_features | |
histgradientboostingregressor__early_stopping | auto |
histgradientboostingregressor__interaction_cst | |
histgradientboostingregressor__l2_regularization | 0.0 |
histgradientboostingregressor__learning_rate | 0.1 |
histgradientboostingregressor__loss | squared_error |
histgradientboostingregressor__max_bins | 255 |
histgradientboostingregressor__max_depth | |
histgradientboostingregressor__max_iter | 100 |
histgradientboostingregressor__max_leaf_nodes | 31 |
histgradientboostingregressor__min_samples_leaf | 20 |
histgradientboostingregressor__monotonic_cst | |
histgradientboostingregressor__n_iter_no_change | 10 |
histgradientboostingregressor__quantile | |
histgradientboostingregressor__random_state | 0 |
histgradientboostingregressor__scoring | loss |
histgradientboostingregressor__tol | 1e-07 |
histgradientboostingregressor__validation_fraction | 0.1 |
histgradientboostingregressor__verbose | 0 |
histgradientboostingregressor__warm_start | False |
Model Plot
Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)])),('histgradientboostingregressor',HistGradientBoostingRegressor(random_state=0))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)])),('histgradientboostingregressor',HistGradientBoostingRegressor(random_state=0))])
ColumnTransformer(transformers=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)])
<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>
SimpleImputer(add_indicator=True)
<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>
OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value',unknown_value=-1)
HistGradientBoostingRegressor(random_state=0)
Evaluation Results
Metric | Value |
---|---|
R2 score | 0.838471 |
MAE | 0.111495 |
How to Get Started with the Model
Use the following code to get started:
import joblib
from skops.hub_utils import download
import json
import pandas as pd
download(repo_id="haizad/ames-housing-gbdt-predictor", dst='ames-housing-gbdt-predictor')
pipeline = joblib.load( "ames-housing-gbdt-predictor/model.pkl")
with open("ames-housing-gbdt-predictor/config.json") as f:
config = json.load(f)
pipeline.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
Model Card Authors
This model card is written by following authors:
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Model Card Contact
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Citation
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BibTeX:
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Intended uses & limitations
This model is not ready to be used in production.
Evaluation
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