Feature Engineering for Existing Features.png

Feature engineering is a crucial stage in any machine learning project. It allows you to use data to define features that enable machine learning algorithms to work properly.

In this course, you will learn the techniques that will help you create new features from existing features. You’ll start by diving into label encoding which is crucial for converting categorical features into numerical. You’ll also learn about other various types of encoding such as: one-hot, count, and mean, all of which are important for feature engineering. 

In the remaining chapters, you’ll learn about feature interaction and datetime features. In all, this course will show you the many different ways you can create features from existing ones.

Feature Engineering for Machine Learning

## What is mean encoding？

**Mean encoding** uses the mean of the target value as a new feature. It's usually done for classification tasks, particularly a binary classification. The mean value is always on the target value. However, mean encoding can be performed on any numerical features, not only the target value. Below is an illustration of mean encoding that is not on the target value.

# What is mean encoding？

**Mean encoding** uses the mean of the target value as a new feature. It's usually done for classification tasks, particularly a binary classification. The mean value is always on the target value. However, mean encoding can be performed on any numerical features, not only the target value. Below is an illustration of mean encoding that is not on the target value.

Learn how to use mean encoding for categorical features.

__default

Mean Encoding

What is mean encoding？