Feature Selection with Stochastic Optimization Algorithms
Typically, a simpler and better-performing machine learning model can be developed by removing input features (columns) from the training dataset.
This is called feature selection and there are many different types of algorithms that can be used.
It is possible to frame the problem of feature selection as an optimization problem. In the case that there are few input features, all possible combinations of input features can be evaluated and the best subset found definitively. In the case of a vast number of input features, a stochastic optimization algorithm can be used to explore the search space and find an effective subset of features.
In this tutorial, you will discover how to use optimization algorithms for feature selection in