Gradient Boosting Out-of-Bag Estimates

# Introduction

This lab will guide you through implementing a Gradient Boosting Classifier with out-of-bag (OOB) estimates using the scikit-learn library in Python. OOB estimates are an alternative to cross-validation estimates and can be computed on-the-fly without the need for repeated model fitting. This lab will cover the following steps:

1. Generate data
2. Fit classifier with OOB estimates
3. Estimate best number of iterations using cross-validation
4. Compute best number of iterations for test data
5. Plot the results

## 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.