This post is the first of a series of posts on model parameter tuning: We will see code to tune Random Forest models with a technique called grid search. Random Forests is a popular ensemble learning method, introduced in its “current” form by Leo Breiman in a same-titled paper. Random forests can be used for classification or regression and offer “protection” from the overfitting that is sometimes observed in single decision trees.