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 )

Examples

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Train a predictor function on labeled examples:

Look at the information about the predictor:

Extract option information that can be used for retraining:

Predict a new example:

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" :

Visualize the PDF obtained:

Obtain information about the distribution:

Use the "DistributionSmoothing" option to train a classifier:

Use the mushrooms training set to train a classifier with the default value of "DistributionSmoothing":

Train a second classifier using a large "DistributionSmoothing":

Compare the probabilities for examples from a test set:

Use the "FeatureFraction" option to train a classifier:

Use the mushrooms training set to train two classifiers with different values of "FeatureFraction":

Look at the accuracy of these classifiers on a test set: