--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: model.pkl widget: structuredData: BsmtFinSF1: - 1280 - 1464 - 0 BsmtUnfSF: - 402 - 536 - 795 Condition2: - Norm - Norm - Norm ExterQual: - Ex - Gd - Gd Foundation: - PConc - PConc - PConc GarageCars: - 3 - 3 - 1 GarageType: - BuiltIn - Attchd - Detchd Heating: - GasA - GasA - GasA HeatingQC: - Ex - Ex - TA HouseStyle: - 2Story - 1Story - 2.5Fin MSSubClass: - 60 - 20 - 75 MasVnrArea: - 272.0 - 246.0 - 0.0 MasVnrType: - Stone - Stone - .nan MiscFeature: - .nan - .nan - .nan MoSold: - 8 - 7 - 3 OverallQual: - 10 - 8 - 4 Street: - Pave - Pave - Pave TotalBsmtSF: - 1682 - 2000 - 795 YearRemodAdd: - 2008 - 2005 - 1950 YrSold: - 2008 - 2007 - 2006 --- # Model description This is a gradient boosted regression model trained on ames housing dataset from OpenML. ## Intended uses & limitations [More Information Needed] ## Training Procedure [More Information Needed] ### Hyperparameters
Click to expand | Hyperparameter | Value | |----------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('columntransformer', ColumnTransformer(transformers=[('simpleimputer',
SimpleImputer(add_indicator=True),
),
('ordinalencoder',
OrdinalEncoder(encoded_missing_value=-2,
handle_unknown='use_encoded_value',
unknown_value=-1),
)])), ('histgradientboostingregressor', HistGradientBoostingRegressor(random_state=0))] | | verbose | False | | columntransformer | ColumnTransformer(transformers=[('simpleimputer',
SimpleImputer(add_indicator=True),
),
('ordinalencoder',
OrdinalEncoder(encoded_missing_value=-2,
handle_unknown='use_encoded_value',
unknown_value=-1),
)]) | | 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), ), ('ordinalencoder', OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value',
unknown_value=-1), )] | | 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 | | | 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.
## Evaluation Results | Metric | Value | |----------|----------| | R2 score | 0.838471 | | MAE | 0.111495 | # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # Intended uses & limitations This model is not ready to be used in production. # Evaluation ![Evaluation](evaluation.png)