"DecisionTree" (Machine Learning Method)

Details & Suboptions

  • A decision tree is a flow chartlike 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"1regularization parameter
    "FeatureFraction"1the fraction of features to be randomly selected for training (only in Classify and Predict)

Examples

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Basic Examples  (3)

Train a predictor function on labeled examples:

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Look at the information about the predictor:

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Extract option information that can be used for retraining:

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Predict a new example:

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Generate some data and visualize it:

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Train a predictor function on it:

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Compare the data with the predicted values and look at the standard deviation:

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Learn a distribution using the method "DecisionTree":

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Visualize the PDF obtained:

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Obtain information about the distribution:

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Options  (4)