Setting parameters in Random Forests

The Random Forest implementation in scikit-learn is called RandomForestClassifier, and it has a number of parameters. As Random Forests use many instances of DecisionTreeClassifier, they share many of the same parameters such as the criterion (Gini Impurity or Entropy/information gain), max_features, and min_samples_split.

There are some new parameters that are used in the ensemble process:

• n_estimators: This dictates how many decision trees should be built. A higher value will take longer to run, but will (probably) result in a higher accuracy.
• oob_score: If true, the method is tested using samples that aren't in the random subsamples chosen for training the decision trees.
• n_jobs: This specifies the number of cores to use when training the decision trees in parallel.

The scikit-learn package uses a library called Joblib for inbuilt parallelization. This parameter dictates how many cores to use. By default, only a single core is used--if you have more cores, you can increase this, or set it to -1 to use all cores.