Feature Selection with caret’s Genetic Algorithm Option

by Joseph Rickert If there is anything that experienced machine learning practitioners are likely to agree on, it would be the importance of careful and thoughtful feature engineering. The judicious selection of which predictor variables to include in a model often has a more beneficial effect on overall classifier performance than the choice of the classification algorithm itself. This is one reason why classification algorithms that automatically include feature selection such as glmnet, gbm or random forests top the list of “go to” algorithms for many practitioners. There are occasions, however, when you find yourself for one reason or another...

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