Add Spline Transformer

Describe the workflow you want to enable

I propose to add a SplineTransformer to preprocessing. This is similiar to PolynomialFeatures, but gives more flexibility (and numerical stability) for linear models to deal with continuous numerical features.

from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import SplineTransformer
from sklearn.pipeline import make_pipeline

# get data X, y
...
model = make_pipeline(SplineTransformer(degree=3, n_knots=20,
                                        positioning='quantile'),
                      LogisticRegression())
model.fit(X, y)

Describe your proposed solution

Add SplineTransformer and internally use scipy for splines. Start with - 1-dimensional b-splines - equidistant knots - quantile based knots

Additional context

Patsy has an implementation of those that matches the R versions.

References

Eilers, Marx “Flexible Smoothing with B-splines and Penalties” passes the scikit-learn inclusion criteria by some margin 😏

[thomasjpfan]

Me and @amueller have been interested in splines in the context of GAMs which would start with 1-D splines for each feature. I have been planning on pushing this forward for scikit-learn.

• R’s mgcv
• How Interpretable and Trustworthy are GAMs?

[jnothman]

I think a lot of the feature-based machine learning community could learn more about spline bases in predictive modelling, and it would be valuable to have these available and discussed.