What are some effective techniques for variable selection in machine learning?

What are some effective techniques for variable selection in machine learning?

There are several techniques for variable selection, including filter methods, wrapper methods, and embedded methods. Would you like to know more about a specific one?

Can you explain filter methods?

Filter methods assess the relevance of variables by their intrinsic properties, often using statistical tests. They are independent of any machine learning algorithm.

What about wrapper methods?

Wrapper methods evaluate subsets of variables by training a model on them and assessing performance. They can be computationally expensive but often yield better results.

And embedded methods?

Embedded methods perform variable selection as part of the model training process, integrating the selection and learning steps. Examples include Lasso and decision trees.