With univariate feature selection, scoring functions will come to the forefront again. This time, they will define the comparable measure by which we can eliminate features.
In this recipe, we'll fit a regression model with around 10,000 features, but only 1,000 points. We'll walk through the various univariate feature selection methods:
from sklearn import datasets X, y = datasets.make_regression(1000, 10000)
Now that we have the data, we will compare the features that are included with the various methods. This is actually a very common situation when you're dealing with text analysis or some areas of bioinformatics.