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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 the weight of evidence encoding?

Weight of evidence (**WOE**) is a technique used to encode categorical features for classification tasks. It's a measure of evidence on one side of an issue compared with the evidence on the other side of the issue.  For simplicity, in this course, we only focus on binary classification.

$$WOE = \ln (\frac{p(1)}{p(0)})$$

- $p(1)$ is the probability of the label `1` for one category value.
- $p(0)$ is the probability of the label `0` for one category value.

# What is the weight of evidence encoding?

Weight of evidence (**WOE**) is a technique used to encode categorical features for classification tasks. It's a measure of evidence on one side of an issue compared with the evidence on the other side of the issue.  For simplicity, in this course, we only focus on binary classification.

$$WOE = \ln (\frac{p(1)}{p(0)})$$

- $p(1)$ is the probability of the label `1` for one category value.
- $p(0)$ is the probability of the label `0` for one category value.

Learn how to use the weight of evidence encoding for categorical features.


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Weight of Evidence Encoding

What is the weight of evidence encoding?