How do filter, wrapper, and embedded methods for feature selection differ from each other?
- By the bias-variance tradeoff
- By the computational complexity
- By the problem-solving approach
- By their use of machine learning models
Filter methods for feature selection evaluate the relevance of the input features based on their correlation with the target variable, and do not involve the use of any specific machine learning algorithm. Wrapper methods involve the use of a specific machine learning algorithm and select features that contribute to the performance of the model. Embedded methods integrate feature selection as part of the model training process.
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