Learn the essential techniques to create new ML features from existing features. Improve the performance of your ML algorithms How to convert categorical data to numerical data.We’ll provide some real-world examples with Sklearn and Pandas. In this guide, we will introduce you to one hot encoding and show you when to use it in your ML models. One hot encoding is a crucial part of feature engineering for machine learning. One hot encoding is a process of converting categorical data variables so they can be provided to machine learning algorithms to improve predictions. Most machine learning tutorials and tools require you to prepare data before it can be fit to a particular ML model. But, what is one hot encoding, and why do we use it? Even the Sklearn documentation tells you to “encode categorical integer features using a one-hot scheme”. If you’re in the field of data science, you’ve probably heard the term “one hot encoding”.
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