Feature Scaling

Introduction

Feature scaling is a text preprocessing technique that ensures that different features in a dataset are on a similar scale to improve the performance of machine-learning models. It’s important to note that we don’t apply it directly to text. Instead, we apply it to the result of other text representation techniques, such as BoW or TF-IDF. These techniques convert the text into numerical representations, to which we can later apply feature scaling.

Reasons for feature scaling

Here are some reasons why feature scaling is important in text preprocessing: