Different ways to choose important features can really change how well a model works in supervised learning. Here’s a simple way to compare them:
Filter Methods: These are fast and easy. They use math tests to rank features. They are good for cleaning up data first, but they might miss how features work together.
Wrapper Methods: These look at groups of features based on how well the model performs. They can improve accuracy a lot, but they take up a lot of computing power and time.
Embedded Methods: These include feature selection right into the model training. They save time and usually give good results.
From what I've seen, using a mix of these methods usually works the best!
Different ways to choose important features can really change how well a model works in supervised learning. Here’s a simple way to compare them:
Filter Methods: These are fast and easy. They use math tests to rank features. They are good for cleaning up data first, but they might miss how features work together.
Wrapper Methods: These look at groups of features based on how well the model performs. They can improve accuracy a lot, but they take up a lot of computing power and time.
Embedded Methods: These include feature selection right into the model training. They save time and usually give good results.
From what I've seen, using a mix of these methods usually works the best!