Feature Selection (Wrapper Methods)
Explore how wrapper methods help in feature selection by iteratively adding or removing features based on model accuracy. Understand techniques like forward selection, backward elimination, exhaustive search, and recursive feature elimination to choose the best feature subset for machine learning models using practical approaches and scikit-learn utilities.
We'll cover the following...
Wrapper Methods
In Wrapper Methods the problem of Feature Selection is reduced to a search problem. A model is built using a set of features and its accuracy is recorded. Based on the accuracy, more features are added or removed, and the process is repeated. We have the following methods in Wrapper Methods.
Forward Selection
Forward Selection is an iterative method. In this method, we start with one feature and we keep on adding features until no improvement in the model is observed. The search is stopped after a pre-set criteria is met. This is ...