Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Embedded Methods

The video tutorial discusses three feature selection methods: filter, wrapper, and embedded. Filter methods are fast but lack model specificity. Wrapper methods are model-specific but time-consuming due to repeated training. Embedded methods combine the advantages of both, being fast and model-specific by training once and using weights to determine feature importance. An example using L1 regularization (lasso regression) illustrates how embedded methods work. The video concludes with a comparison of the methods and a preview of future topics, including implementation in Python.

What are the main differences between wrapper methods and embedded methods in feature selection?

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Explain how L1 regularization works in the context of feature selection.

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What are the advantages of using embedded methods over wrapper methods?

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Describe the process of how embedded methods select features.

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What challenges are associated with wrapper methods in feature selection?

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How does model specificity affect the performance of feature selection methods?

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In what scenarios might filter methods be preferred over wrapper or embedded methods?

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