What are the main differences between wrapper methods and embedded methods in feature selection?
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Embedded Methods

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1.
OPEN ENDED QUESTION
3 mins • 1 pt
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2.
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Explain how L1 regularization works in the context of feature selection.
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3.
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What are the advantages of using embedded methods over wrapper methods?
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4.
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Describe the process of how embedded methods select features.
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5.
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What challenges are associated with wrapper methods in feature selection?
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6.
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How does model specificity affect the performance of feature selection methods?
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7.
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In what scenarios might filter methods be preferred over wrapper or embedded methods?
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