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--- |
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license: mit |
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library_name: sklearn |
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tags: |
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- sklearn |
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- skops |
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- tabular-classification |
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model_format: pickle |
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model_file: model.pkl |
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widget: |
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structuredData: |
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BsmtFinSF1: |
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- 1280 |
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- 1464 |
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- 0 |
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BsmtUnfSF: |
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- 402 |
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- 536 |
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- 795 |
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Condition2: |
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- Norm |
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- Norm |
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- Norm |
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ExterQual: |
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- Ex |
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- Gd |
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- Gd |
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Foundation: |
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- PConc |
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- PConc |
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- PConc |
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GarageCars: |
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- 3 |
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- 3 |
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- 1 |
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GarageType: |
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- BuiltIn |
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- Attchd |
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- Detchd |
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Heating: |
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- GasA |
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- GasA |
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- GasA |
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HeatingQC: |
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- Ex |
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- Ex |
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- TA |
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HouseStyle: |
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- 2Story |
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- 1Story |
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- 2.5Fin |
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MSSubClass: |
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- 60 |
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- 20 |
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- 75 |
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MasVnrArea: |
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- 272.0 |
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- 246.0 |
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- 0.0 |
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MasVnrType: |
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- Stone |
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- Stone |
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- .nan |
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MiscFeature: |
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- .nan |
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- .nan |
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- .nan |
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MoSold: |
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- 8 |
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- 7 |
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- 3 |
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OverallQual: |
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- 10 |
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- 8 |
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- 4 |
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Street: |
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- Pave |
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- Pave |
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- Pave |
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TotalBsmtSF: |
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- 1682 |
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- 2000 |
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- 795 |
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YearRemodAdd: |
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- 2008 |
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- 2005 |
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- 1950 |
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YrSold: |
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- 2008 |
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- 2007 |
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- 2006 |
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--- |
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# Model description |
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This is a Lasso regression model trained on ames housing dataset from OpenML |
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## Intended uses & limitations |
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This model is not ready to be used in production. |
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## Training Procedure |
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[More Information Needed] |
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### Hyperparameters |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|-----------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| memory | | |
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| steps | [('columntransformer', ColumnTransformer(transformers=[('pipeline',<br /> Pipeline(steps=[('standardscaler',<br /> StandardScaler()),<br /> ('simpleimputer',<br /> SimpleImputer(add_indicator=True))]),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),<br /> ('onehotencoder',<br /> OneHotEncoder(handle_unknown='ignore'),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)])), ('lassocv', LassoCV())] | |
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| verbose | False | |
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| columntransformer | ColumnTransformer(transformers=[('pipeline',<br /> Pipeline(steps=[('standardscaler',<br /> StandardScaler()),<br /> ('simpleimputer',<br /> SimpleImputer(add_indicator=True))]),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),<br /> ('onehotencoder',<br /> OneHotEncoder(handle_unknown='ignore'),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)]) | |
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| lassocv | LassoCV() | |
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| columntransformer__n_jobs | | |
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| columntransformer__remainder | drop | |
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| columntransformer__sparse_threshold | 0.3 | |
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| columntransformer__transformer_weights | | |
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| columntransformer__transformers | [('pipeline', Pipeline(steps=[('standardscaler', StandardScaler()),<br /> ('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>)] | |
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| columntransformer__verbose | False | |
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| columntransformer__verbose_feature_names_out | True | |
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| columntransformer__pipeline | Pipeline(steps=[('standardscaler', StandardScaler()),<br /> ('simpleimputer', SimpleImputer(add_indicator=True))]) | |
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| columntransformer__onehotencoder | OneHotEncoder(handle_unknown='ignore') | |
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| columntransformer__pipeline__memory | | |
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| columntransformer__pipeline__steps | [('standardscaler', StandardScaler()), ('simpleimputer', SimpleImputer(add_indicator=True))] | |
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| columntransformer__pipeline__verbose | False | |
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| columntransformer__pipeline__standardscaler | StandardScaler() | |
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| columntransformer__pipeline__simpleimputer | SimpleImputer(add_indicator=True) | |
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| columntransformer__pipeline__standardscaler__copy | True | |
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| columntransformer__pipeline__standardscaler__with_mean | True | |
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| columntransformer__pipeline__standardscaler__with_std | True | |
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| columntransformer__pipeline__simpleimputer__add_indicator | True | |
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| columntransformer__pipeline__simpleimputer__copy | True | |
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| columntransformer__pipeline__simpleimputer__fill_value | | |
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| columntransformer__pipeline__simpleimputer__keep_empty_features | False | |
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| columntransformer__pipeline__simpleimputer__missing_values | nan | |
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| columntransformer__pipeline__simpleimputer__strategy | mean | |
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| columntransformer__pipeline__simpleimputer__verbose | deprecated | |
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| columntransformer__onehotencoder__categories | auto | |
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| columntransformer__onehotencoder__drop | | |
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| columntransformer__onehotencoder__dtype | <class 'numpy.float64'> | |
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| columntransformer__onehotencoder__handle_unknown | ignore | |
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| columntransformer__onehotencoder__max_categories | | |
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| columntransformer__onehotencoder__min_frequency | | |
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| columntransformer__onehotencoder__sparse | deprecated | |
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| columntransformer__onehotencoder__sparse_output | True | |
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| lassocv__alphas | | |
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| lassocv__copy_X | True | |
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| lassocv__cv | | |
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| lassocv__eps | 0.001 | |
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| lassocv__fit_intercept | True | |
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| lassocv__max_iter | 1000 | |
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| lassocv__n_alphas | 100 | |
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| lassocv__n_jobs | | |
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| lassocv__positive | False | |
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| lassocv__precompute | auto | |
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| lassocv__random_state | | |
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| lassocv__selection | cyclic | |
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| lassocv__tol | 0.0001 | |
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| lassocv__verbose | False | |
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</details> |
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### Model Plot |
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<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>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())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>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())])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>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>)])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline</label><div class="sk-toggleable__content"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0></pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(add_indicator=True)</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">onehotencoder</label><div class="sk-toggleable__content"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0></pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">LassoCV</label><div class="sk-toggleable__content"><pre>LassoCV()</pre></div></div></div></div></div></div></div> |
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## Evaluation Results |
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| Metric | Value | |
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|----------|----------| |
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| R2 score | 0.753308 | |
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| MAE | 0.112742 | |
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# How to Get Started with the Model |
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[More Information Needed] |
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# Model Card Authors |
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This model card is written by following authors: |
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[More Information Needed] |
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# Model Card Contact |
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You can contact the model card authors through following channels: |
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[More Information Needed] |
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# Citation |
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Below you can find information related to citation. |
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**BibTeX:** |
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``` |
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[More Information Needed] |
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``` |
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# Evaluation |
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