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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 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

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-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

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[More Information Needed]

Model Card Contact

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Citation

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BibTeX:

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Evaluation

evaluation