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 Lasso 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=[('pipeline', Pipeline(steps=[('standardscaler', StandardScaler()), ('simpleimputer', SimpleImputer(add_indicator=True))]), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>), ('onehotencoder', OneHotEncoder(handle_unknown='ignore'), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)])), ('lassocv', LassoCV())]
verbose	False
columntransformer	ColumnTransformer(transformers=[('pipeline', Pipeline(steps=[('standardscaler', StandardScaler()), ('simpleimputer', SimpleImputer(add_indicator=True))]), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>), ('onehotencoder', OneHotEncoder(handle_unknown='ignore'), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)])
lassocv	LassoCV()
columntransformer__n_jobs
columntransformer__remainder	drop
columntransformer__sparse_threshold	0.3
columntransformer__transformer_weights
columntransformer__transformers	[('pipeline', Pipeline(steps=[('standardscaler', StandardScaler()), ('simpleimputer', SimpleImputer(add_indicator=True))]), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>), ('onehotencoder', OneHotEncoder(handle_unknown='ignore'), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)]
columntransformer__verbose	False
columntransformer__verbose_feature_names_out	True
columntransformer__pipeline	Pipeline(steps=[('standardscaler', StandardScaler()), ('simpleimputer', SimpleImputer(add_indicator=True))])
columntransformer__onehotencoder	OneHotEncoder(handle_unknown='ignore')
columntransformer__pipeline__memory
columntransformer__pipeline__steps	[('standardscaler', StandardScaler()), ('simpleimputer', SimpleImputer(add_indicator=True))]
columntransformer__pipeline__verbose	False
columntransformer__pipeline__standardscaler	StandardScaler()
columntransformer__pipeline__simpleimputer	SimpleImputer(add_indicator=True)
columntransformer__pipeline__standardscaler__copy	True
columntransformer__pipeline__standardscaler__with_mean	True
columntransformer__pipeline__standardscaler__with_std	True
columntransformer__pipeline__simpleimputer__add_indicator	True
columntransformer__pipeline__simpleimputer__copy	True
columntransformer__pipeline__simpleimputer__fill_value
columntransformer__pipeline__simpleimputer__keep_empty_features	False
columntransformer__pipeline__simpleimputer__missing_values	nan
columntransformer__pipeline__simpleimputer__strategy	mean
columntransformer__pipeline__simpleimputer__verbose	deprecated
columntransformer__onehotencoder__categories	auto
columntransformer__onehotencoder__drop
columntransformer__onehotencoder__dtype	<class 'numpy.float64'>
columntransformer__onehotencoder__handle_unknown	ignore
columntransformer__onehotencoder__max_categories
columntransformer__onehotencoder__min_frequency
columntransformer__onehotencoder__sparse	deprecated
columntransformer__onehotencoder__sparse_output	True
lassocv__alphas
lassocv__copy_X	True
lassocv__cv
lassocv__eps	0.001
lassocv__fit_intercept	True
lassocv__max_iter	1000
lassocv__n_alphas	100
lassocv__n_jobs
lassocv__positive	False
lassocv__precompute	auto
lassocv__random_state
lassocv__selection	cyclic
lassocv__tol	0.0001
lassocv__verbose	False

Model Plot

Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('pipeline',Pipeline(steps=[('standardscaler',StandardScaler()),('simpleimputer',SimpleImputer(add_indicator=True))]),<sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),('onehotencoder',OneHotEncoder(handle_unknown='ignore'),<sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)])),('lassocv', LassoCV())])

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Evaluation Results

Metric	Value
R2 score	0.753308
MAE	0.112742

How to Get Started with the Model

[More Information Needed]

Model Card Authors

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Citation

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BibTeX:

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