Learning to transform categorical data into a format that a machine learning model can understand
When studying machine learning, it is essential to understand the inner workings of the most basic algorithms. Doing so helps in understanding how algorithms operate in popular libraries and frameworks, how to debug them, choose better hyperparameters more easily, and determine which algorithm is best suited for a given problem.
While algorithms are at the core of machine learning, they cannot produce effective results without high-quality data. Since data can be a scarce resource in some problems, it is crucial to learn how to preprocess it effectively to extract maximum value. Moreover, improperly preprocessed data can deteriorate an algorithm’s performance.
In this article, we will examine one-hot encoding, one of the most fundamental techniques used for data preprocessing. To do this effectively, we will first understand the motivation behind data encoding in general and then explore its principles and…