# Introduction Feature selection is an important step in machine learning. It involves selecting the most relevant features from a dataset to improve the accuracy and performance of the model. In scikit-learn, the `sklearn.feature_selection` module provides various methods for feature selection and dimensionality reduction. This lab will guide you through the process of feature selection using scikit-learn. We will cover techniques such as removing features with low variance, univariate feature selection, recursive feature elimination, and feature selection using SelectFromModel. ## 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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