--- 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 This model is not ready to be used in production. ## 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))])
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## 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: ```python 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: [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)