adaBoostRegressor#

swordfish.function.adaBoostRegressor()#

Fit an AdaBoost regression model.

Parameters:

  • ds (Constant) – Tthe data sources to be trained. It can be generated with function sqlDS.

  • yColName (Constant) – A string indicating the name of the dependent variable column in the data sources.

  • xColNames (Constant) – A string scalar/vector indicating the names of the feature columns in the data sources.

  • maxFeatures (Constant, optional) – An integer or a floating number indicating the number of features to consider when looking for the best split, by default DFLT.

  • numTrees (Constant, optional) – A positive integer indicating the number of trees, by default DFLT.

  • numBins (Constant, optional) – A positive integer indicating the number of bins used when discretizing continuous features, by default DFLT.

  • maxDepth (Constant, optional) – A positive integer indicating the maximum depth of a tree, by default DFLT.

  • minImpurityDecrease (Constant, optional) – A node will be split if this split induces a decrease of impurity greater than or equal to this value, by default DFLT.

  • learningRate (Constant, optional) – A positive floating number indicating the contribution of a regressor to the next regressor, by default DFLT.

  • loss (Constant, optional) – A string indicating the loss function to use when updating the weights after each boosting iteration, by default DFLT.

  • randomSeed (Constant, optional) – The seed used by the random number generator, by default DFLT.

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