What are some common feature selection techniques in machine learning?

What are some common feature selection techniques in machine learning?

Common feature selection techniques include filter methods, wrapper methods, and embedded methods. Would you like to know more about each?

Yes, please explain filter methods.

Filter methods evaluate features based on their statistical properties, like correlation with the target variable, and select the best ones.

What about wrapper methods?

Wrapper methods use a predictive model to evaluate combinations of features and select the best subset based on model performance.

And embedded methods?

Embedded methods perform feature selection as part of the model training process, integrating it with the learning algorithm.