"DecisionTree" (Machine Learning Method)
- Method for Predict, Classify and LearnDistribution.
- Use a decision tree to model class probabilities, value predictions or probability densities.
Details & Suboptions
- A decision tree is a flow chart–like structure in which each internal node represents a "test" on a feature, each branch represents the outcome of the test, and each leaf represents a class distribution, value distribution or probability density.
- For Classify and Predict, the tree is constructed using the CART algorithm.
- For LearnDistribution, the splits are determined using an information criterion trading off the likelihood and the complexity of the model.
- The following options can be given:
"DistributionSmoothing" 1 regularization parameter "FeatureFraction" 1 the fraction of features to be randomly selected for training (only in Classify and Predict)
Examplesopen allclose all
Basic Examples (3)
Train a predictor function on labeled examples:
Look at the information about the predictor:
Extract option information that can be used for retraining:
Generate some data and visualize it:
Train a predictor function on it:
Compare the data with the predicted values and look at the standard deviation:
Learn a distribution using the method "DecisionTree":