"GradientBoostedTrees"
(Machine Learning Method)

  • Method for Classify and Predict.
  • Predict the value or class of an example using an ensemble of decision trees.
  • Trees are trained sequentially following the boosting meta-algorithm.
  • Details & Suboptions
  • Gradient boosting is a machine learning technique for regression and classification problems that produces a prediction model in the form of an ensemble of trees. Trees are trained sequentially with the goal of compensating the weaknesses of previous trees. The current implementation uses the LightGBM framework in the back end.
  • The following options can be given:
  • MaxTrainingRounds50number of boosting rounds
    "BoostingMethod""Gradient"the method to use
    "L1Regularization"0L1 regularization parameter
    "L2Regularization"0L2 regularization parameter
    "LeafSize"Automaticminimum number of data samples in one leaf
    "LearningRate"Automaticlearning rate used in gradient descent
    "LeavesNumber"Automaticminimum number of leaves in one tree
    "MaxDepth"6maximum depth of each tree
  • Examples

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    Basic Examples  (2)

    Train a predictor function on labeled examples:

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    Look at its PredictorInformation:

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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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    Options  (8)

    See Also

    Classify  Predict  ClassifierFunction  PredictorFunction  ClassifierMeasurements  PredictorMeasurements  ClassifierInformation  PredictorInformation  SequencePredict  ClusterClassify

    Related Demonstrations

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