Feature Selection: Wrapper Methods
Explore wrapper methods for feature selection in machine learning. Learn to apply forward selection, backward elimination, exhaustive search, and recursive feature elimination to identify the most impactful features for improving model performance and accuracy.
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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 criterion is met. This ...