"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"0.5regularization parameter
    "FeatureFraction"Automaticthe fraction of features to be randomly selected to train each tree
    "LeafSize"Automaticthe maximum number of examples in each leaf
    "TreeNumber"Automaticthe number of trees in the forest
  • "FeatureFraction", "LeafSize" and "DistributionSmoothing" can be used to control overfitting.
  • Examples

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    Basic Examples  (3)

    Train a predictor on labeled examples:

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    Look at the PredictorInformation:

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    Predict a new example:

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    Train a classifier function on labeled examples:

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    Plot the probability that the class of an example is "A" or "B" as a function of the feature and compare them:

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    Train a predictor function on labeled data:

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

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    Options  (6)

    See Also

    Classify  Predict  ClassifierFunction  PredictorFunction  ClassifierMeasurements  PredictorMeasurements  ClassifierInformation  PredictorInformation  SequencePredict  ClusterClassify

    Related Demonstrations

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