# Introduction This lab demonstrates the use of Scikit-learn's Lasso regression algorithm on dense and sparse data. The Lasso algorithm is a linear regression method that adds a penalty to the regression coefficients. This penalty encourages the model to produce sparse coefficients. The Lasso algorithm is useful in situations where the number of features is large compared to the number of samples. ## 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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