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ames-housing-gbdt-predictor

Tabular Classification
Scikit-learn
skops
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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))])
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

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

Below you can find information related to citation.

BibTeX:

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Intended uses & limitations

This model is not ready to be used in production.

Evaluation

Evaluation