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 ...