haizad/ames-housing-gbdt-predictor

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))])

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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:

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

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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