# Introduction This lab compares different anomaly detection algorithms on two-dimensional datasets. The datasets contain one or two modes (regions of high density) to illustrate the ability of algorithms to cope with multimodal data. For each dataset, 15% of samples are generated as random uniform noise. Decision boundaries between inliers and outliers are displayed in black except for Local Outlier Factor (LOF) as it has no predict method to be applied on new data when it is used for outlier detection. ## 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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