What are some common feature selection methods in machine learning?

What are some common feature selection methods in machine learning?

Some common feature selection methods include filter methods, wrapper methods, and embedded methods.

Can you explain what filter methods are?

Filter methods evaluate features based on their statistical properties, selecting those that have the strongest relationship with the target variable.

What about wrapper methods?

Wrapper methods evaluate subsets of features by training a model on them and using model performance to assess their usefulness.

And embedded methods?

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