Building robust ML baselines with tabular_pipeline()

The tabular_pipeline() is a function that, given a scikit-learn estimator, returns a full scikit-learn Pipeline that contains a TableVectorizer followed by the given estimator. If the estimator is a linear model (e.g., Ridge, LogisticRegression), tabular_pipeline() adds a StandardScaler and a SimpleImputer to the pipeline.

>>> from sklearn.linear_model import LinearRegression
>>> from skrub import tabular_pipeline
>>> tabular_pipeline(LinearRegression())
Pipeline(steps=[('tablevectorizer',
                 TableVectorizer(datetime=DatetimeEncoder(periodic_encoding='spline'))),
                ('simpleimputer', SimpleImputer(add_indicator=True)),
                ('standardscaler', StandardScaler()),
                ('linearregression', LinearRegression())])

It is also possible to call the function with the name of the task that must be performed (regression/regressor, classification/classifier) to build a pipeline that uses a HistGradientBoostingRegressor/HistGradientBoostingClassifier.

>>> from skrub import tabular_pipeline
>>> tabular_pipeline("regression")
Pipeline(steps=[('tablevectorizer',
                 TableVectorizer(...),
                ('histgradientboostingregressor',
                 HistGradientBoostingRegressor())])

The pipeline prepared by tabular_pipeline() is a strong first baseline for most problems, but may not beat properly tuned ad-hoc pipelines.

Parameter values choice of TableVectorizer when using the tabular_pipeline() function

Parameter	RandomForest models	HistGradientBoosting models	Linear models and others
Low-cardinality encoder	OrdinalEncoder	Native support (1)	OneHotEncoder
High-cardinality encoder	StringEncoder	StringEncoder	StringEncoder
Numeric preprocessor	No processing	No processing	StandardScaler
Date preprocessor	DatetimeEncoder	DatetimeEncoder	DatetimeEncoder with spline encoding
Missing value strategy	Native support (2)	Native support	SimpleImputer

Note

⁽¹⁾ if scikit-learn installed is lower than 1.4, then OrdinalEncoder is used since native support for categorical features is not available.

⁽²⁾ if scikit-learn installed is lower than 1.4, then SimpleImputer is used since native support for missing values is not available.