Feature Engineering for Modern Machine Learning with Scikit-Learn

2.2 Feature Engineering for Classification and Regression Models

Feature engineering for classification and regression models is a critical process that enhances predictive accuracy by creating features that capture underlying patterns in the data. Unlike unsupervised learning techniques such as clustering or exploratory analysis, classification and regression models rely on labeled data to predict a specific target variable. This approach is essential whether the goal is to classify customers by loyalty level, predict house prices, or forecast customer lifetime value.

The process of feature engineering involves several key strategies:

• Feature Creation: Developing new features that encapsulate relevant information from existing data. For example, ...