--- 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: - 272.0 - 246.0 - 0.0 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))]),
),
('onehotencoder',
OneHotEncoder(handle_unknown='ignore'),
)])), ('lassocv', LassoCV())] | | verbose | False | | columntransformer | ColumnTransformer(transformers=[('pipeline',
Pipeline(steps=[('standardscaler',
StandardScaler()),
('simpleimputer',
SimpleImputer(add_indicator=True))]),
),
('onehotencoder',
OneHotEncoder(handle_unknown='ignore'),
)]) | | 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))]), ), ('onehotencoder', OneHotEncoder(handle_unknown='ignore'), )] | | 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 | | | 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 Use the following code to get started: ```python import joblib from skops.hub_utils import download import json import pandas as pd download(repo_id="haizad/ames-housing-lasso-predictor", dst='ames-housing-lasso-predictor') pipeline = joblib.load( "ames-housing-lasso-predictor/model.pkl") with open("ames-housing-lasso-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 You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # Evaluation ![evaluation](prediction_error.png)