Machine Learning for OpenCV 4 - Second Edition

Training and testing the random forest

We continue to follow our best practice to split the data into training and test sets:

In [8]: from sklearn.model_selection import train_test_split...     X_train, X_test, y_train, y_test = train_test_split(...         X, y, random_state=21...     )

Then, we are ready to apply a random forest to the data:

In [9]: import cv2...     rtree = cv2.ml.RTrees_create()

Here, we want to create an ensemble with 50 decision trees:

In [10]: n_trees = 50...      eps = 0.01...      criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS,...                  n_trees, eps)...      rtree.setTermCriteria(criteria)

Because we have a large number of categories (that is, 40), we want to make sure the random forest is set up to handle them accordingly:

In [10]: rtree.setMaxCategories(len(np.unique(y))) ...