Machine Learning for OpenCV

Implementing a random forest with scikit-learn

Alternatively, we can implement random forests using scikit-learn:

In [13]: from sklearn.ensemble import RandomForestClassifier...      forest = RandomForestClassifier(n_estimators=10, random_state=200)

Here, we have a number of options to customize the ensemble:

• n_estimators: This specifies the number of trees in the forest.
• criterion: This specifies the node splitting criterion. Setting criterion='gini' implements the Gini impurity, whereas setting criterion='entropy' implements information gain.
• max_features: This specifies the number (or fraction) of features to consider at each node split.
• max_depth: This specifies the maximum depth of each tree.
• min_samples: This specifies the minimum number ...