Feature Scaling with Python’s Scikit-learn

Author(s): Bindhu Balu

Originally published on Towards AI.

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One of the primary objectives of normalization is to bring the data close to zero. That makes the optimization problem more “numerically stable”.

Now, the scaling using mean and standard deviation assumes that the data is normally distributed, that is, most of the data is sufficiently close to the mean. So shifting the mean to zero ensures that most components of most data points are close to 0. Specifically, 68% of data would be between -1 and 1, as can be seen from the following figure:

In this post we explore 3 methods of feature scaling that are implemented in scikit-learn:

StandardScalerMinMaxScalerRobustScalerNormalizer

The… Read the full blog for free on Medium.

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Published via Towards AI