public sealed class FastTreeBinaryTrainer : Microsoft.ML.Trainers.FastTree.BoostingFastTreeTrainerBase<Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer.Options,Microsoft.ML.Data.BinaryPredictionTransformer<Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.FastTree.FastTreeBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>,Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.FastTree.FastTreeBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>type FastTreeBinaryTrainer = class
inherit BoostingFastTreeTrainerBase<FastTreeBinaryTrainer.Options, BinaryPredictionTransformer<CalibratedModelParametersBase<FastTreeBinaryModelParameters, PlattCalibrator>>, CalibratedModelParametersBase<FastTreeBinaryModelParameters, PlattCalibrator>>Public NotInheritable Class FastTreeBinaryTrainer
Inherits BoostingFastTreeTrainerBase(Of FastTreeBinaryTrainer.Options, BinaryPredictionTransformer(Of CalibratedModelParametersBase(Of FastTreeBinaryModelParameters, PlattCalibrator)), CalibratedModelParametersBase(Of FastTreeBinaryModelParameters, PlattCalibrator))To create this trainer, use FastTree or FastTree(Options).
Input and Output ColumnsThe input label column data must be Boolean. The input features column data must be a known-sized vector of Single.
This trainer outputs the following columns:
Output Column Name Column Type DescriptionScore Single The unbounded score that was calculated by the model. PredictedLabel Boolean The predicted label, based on the sign of the score. A negative score maps to false and a positive score maps to true. Probability Single The probability calculated by calibrating the score of having true as the label. Probability value is in range [0, 1]. Trainer Characteristics Machine learning task Binary classification Is normalization required? No Is caching required? No Required NuGet in addition to Microsoft.ML Microsoft.ML.FastTree Exportable to ONNX Yes Training Algorithm Details
FastTree is an efficient implementation of the MART gradient boosting algorithm. Gradient boosting is a machine learning technique for regression problems. It builds each regression tree in a step-wise fashion, using a predefined loss function to measure the error for each step and corrects for it in the next. So this prediction model is actually an ensemble of weaker prediction models. In regression problems, boosting builds a series of such trees in a step-wise fashion and then selects the optimal tree using an arbitrary differentiable loss function.
MART learns an ensemble of regression trees, which is a decision tree with scalar values in its leaves. A decision (or regression) tree is a binary tree-like flow chart, where at each interior node one decides which of the two child nodes to continue to based on one of the feature values from the input. At each leaf node, a value is returned. In the interior nodes, the decision is based on the test x <= v where x is the value of the feature in the input sample and v is one of the possible values of this feature. The functions that can be produced by a regression tree are all the piece-wise constant functions.
The ensemble of trees is produced by computing, in each step, a regression tree that approximates the gradient of the loss function, and adding it to the previous tree with coefficients that minimize the loss of the new tree. The output of the ensemble produced by MART on a given instance is the sum of the tree outputs.
For more information see:
Check the See Also section for links to examples of the usage.
Fields Properties Methods Extension Methods See alsoRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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