Random forests are an ensemble over a large number of decision trees. The number of trees used is controlled by a parameter called
n_estimators. Below you can see a heatmap of the accuracy of a random forest classifier. Different values of maximum depth (
max_depth) are shown on the vertical axis. Different numbers of estimators (
n_estimators) are shown on the horizontal axis. How does the performance of the classifier depend on these two hyperparameters in this case?