Random forest regression

In this section, we will start by focusing on the prostate data again. Before moving on to the breast cancer and Pima Indian sets. We will use the randomForest package. The general syntax to create a random forest object is to use the randomForest() function and specify the formula and dataset as the two primary arguments. Recall that for regression, the default variable sample per tree iteration is p/3, and for classification, it is the square root of p, where p is equal to the number of predictor variables in the data frame. For larger datasets, in terms of p, you can tune the mtry parameter, which will determine the number of p sampled at each iteration. If p is less than 10 in these examples, we will forgo this ...

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