Recursive Feature Elimination with Cross-Validation

# Introduction

In this lab, we will go through a step-by-step process of implementing Recursive Feature Elimination with Cross-Validation (RFECV) using scikit-learn. RFECV is used for feature selection, which is the process of selecting a subset of relevant features for use in model construction. We will use a classification task with 15 features, out of which 3 are informative, 2 are redundant, and 10 are non-informative.

## VM Tips

After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.