# Introduction In this lab, you will learn how to implement different regularization strategies for Gradient Boosting using scikit-learn. Regularization is a technique that helps prevent overfitting, which is a common problem in machine learning models. We will use the binomial deviance loss function and the make_hastie_10_2 dataset for this lab. ## 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.
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