Machine learning algorithms are parameterized so that they can be best adapted for a given problem. A difficulty is that configuring an algorithm for a given problem can be a project in and of itself.
Like selecting ‘the best’ algorithm for a problem you cannot know before hand which algorithm parameters will be best for a problem. The best thing to do is to investigate empirically with controlled experiments.
The caret R package was designed to make finding optimal parameters for an algorithm very easy. It provides a grid search method for searching parameters, combined with various methods for estimating the performance of a given model.
In this post you will discover 5 recipes that you can use to tune machine learning algorithms to find optimal parameters for your problems using the caret R package.
Read full paper written by Jason at http://bit.ly/1obUfci