Decision Tree Regression

# Introduction

In this lab, we will learn how to use the decision tree regression algorithm to fit a sine curve with additional noisy observation. The decision trees will be used to learn local linear regressions approximating the sine curve. We will see that if the maximum depth of the tree is set too high, the decision trees learn too fine details of the training data and learn from the noise, i.e. they overfit.

## 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.