"RandomForest" (Machine Learning Method)
- Method for Classify and Predict.
- Predict the value or class of an example using an ensemble of decision trees.
Details & Suboptions
- Random forest is an ensemble learning method for classification and regression that operates by constructing a multitude of decision trees. The forest prediction is obtained by taking the most common class or the mean-value tree predictions. Each decision tree is trained on a random subset of the training set and only uses a random subset of the features (bootstrap aggregating algorithm).
- The following options can be given:
"DistributionSmoothing" 0.5 regularization parameter "FeatureFraction" Automatic the fraction of features to be randomly selected to train each tree "LeafSize" Automatic the maximum number of examples in each leaf "TreeNumber" Automatic the number of trees in the forest
- "FeatureFraction", "LeafSize" and "DistributionSmoothing" can be used to control overfitting.
Examplesopen allclose all
Basic Examples (3)
Look at the PredictorInformation: