Feature selection, a technique in feature engineering, plays a key role in building effective machine learning models. Lasso regression, short for Least Absolute Shrinkage and Selection Operator, is a useful tool for selecting important features. It helps reduce model complexity, prevent overfitting, and makes the model easier to understand.
In this tutorial, we'll go through the steps for using Lasso regression to perform feature selection. This tutorial
will cover:
- Brief Explanation of Lasso
- Preparing the data
- Training a Baseline Linear Regression Model
- Applying Lasso for Feature Selection
- Evaluating a Model Using Selected Features
- Conclusion
- Full source code listing
Let's get started.