# Introduction In this lab, we will go through the process of using Isolation Forest for anomaly detection. We will start by generating a dataset with two clusters and some outliers, then train an Isolation Forest model to identify the outliers. Finally, we will visualize the decision boundary of the model to see how it separates the inliers and outliers. ## 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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