To perform classification with the random forest model, we have to import the RandomForestClassifier method. For example, let's take 35 individual trees with a max_depth of 20 for each tree. The max_features parameter tells scikit-learn that, when deciding upon the best split among possible features, we should use the square root of the total number of features that we have. These are all hyperparameters that we can tune.
The following screenshot shows the code and the confusion matrix as the output:
