6.5. Unsupervised dimensionality reduction#

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.

6.5.1. PCA: principal component analysis#

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).


6.5.2. Random projections#

The module: random_projection provides several tools for data reduction by random projections. See the relevant section of the documentation: Random Projection.


6.5.3. Feature agglomeration#

cluster.FeatureAgglomeration applies Hierarchical clustering to group together features that behave similarly.