Compare Random Forest
Random Forest vs Decision Tree
Random Forest
Random Forest is an ensemble learning algorithms that constructs many decision trees during the training. It predicts the mode of the classes for classification tasks and mean prediction of trees for regression tasks.
It is using random subspace method and bagging during tree construction. It has built-in feature importance.
Reference
Breiman Leo, Random Forests, Machine Learning vol.45, pp. 5–32, 2001
Ho, Tin Kam, Random Decision Forests, Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, pp. 278–282, 1995.
License
License for Scikit-Learn implementation of Random Forest: New BSD License
Links
RandomForestClassifier Documentation
RandomForestRegressor Documentation
Articles
How to reduce memory used by Random Forest from Scikit-Learn in Python?
How to save and load Random Forest from Scikit-Learn in Python?
Random Forest Feature Importance Computed in 3 Ways with Python
How to visualize a single Decision Tree from the Random Forest in Scikit-Learn?
How many trees in the Random Forest?