If your number of features is high, it may be useful to reduce it with an unsupervised step prior to supervised steps. Many of the Unsupervised learning methods implement a transform method that can be used to reduce the dimensionality. Below we discuss two specific example of this pattern that are heavily used.
decomposition.PCA looks for a combination of features that capture well the variance of the original features. See Decomposing signals in components (matrix factorization problems).
The module: random_projection provides several tools for data reduction by random projections. See the relevant section of the documentation: Random Projection.
cluster.FeatureAgglomeration applies Hierarchical clustering to group together features that behave similarly.
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https://scikit-learn.org/1.6/modules/unsupervised_reduction.html