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ames-housing-lasso-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 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())])
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 0.753308
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"]))

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[More Information Needed]

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Citation

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BibTeX:

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Evaluation

evaluation