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metadata
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
      - 246
      - 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),
<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

This model card is written by following authors:

[More Information Needed]

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

Evaluation