Step 2: Data minimization techniques for Supervised Learning

If we want to diminish the amount of data employed by the algorithm, we can think of two possible ways of preprocessing the data:

• Reducing the number of features, or

• Reducing the number of data points.

In the following pages we will cover a number of techniques for each option:

Option 1: Reducing featuresOption 2: Reducing data points