# Introduction In this lab, we will demonstrate how to use L1-based regression models to deal with high-dimensional and sparse signals. In particular, we will compare three popular L1-based models: Lasso, Automatic Relevance Determination (ARD), and ElasticNet. We will use a synthetic dataset to illustrate the performance of these models in terms of fitting time, R2 score, and sparsity of estimated coefficients. ## 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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