public sealed class GamRegressionTrainer : Microsoft.ML.Trainers.FastTree.GamTrainerBase<Microsoft.ML.Trainers.FastTree.GamRegressionTrainer.Options,Microsoft.ML.Data.RegressionPredictionTransformer<Microsoft.ML.Trainers.FastTree.GamRegressionModelParameters>,Microsoft.ML.Trainers.FastTree.GamRegressionModelParameters>type GamRegressionTrainer = class
inherit GamTrainerBase<GamRegressionTrainer.Options, RegressionPredictionTransformer<GamRegressionModelParameters>, GamRegressionModelParameters>Public NotInheritable Class GamRegressionTrainer
Inherits GamTrainerBase(Of GamRegressionTrainer.Options, RegressionPredictionTransformer(Of GamRegressionModelParameters), GamRegressionModelParameters)To create this trainer, use Gam or Gam(Options).
Input and Output ColumnsThe input label column data must be Single. 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 predicted by the model. Trainer Characteristics Machine learning task Regression Is normalization required? No Is caching required? No Required NuGet in addition to Microsoft.ML Microsoft.ML.FastTree Exportable to ONNX No Training Algorithm Details
Generalized Additive Models, or GAMs, model the data as a set of linearly independent features similar to a linear model. For each feature, the GAM trainer learns a non-linear function, called a "shape function", that computes the response as a function of the feature's value. (In contrast, a linear model fits a linear response (e.g. a line) to each feature.) To score an input, the outputs of all the shape functions are summed and the score is the total value.
This GAM trainer is implemented using shallow gradient boosted trees (e.g. tree stumps) to learn nonparametric shape functions, and is based on the method described in Lou, Caruana, and Gehrke. "Intelligible Models for Classification and Regression." KDD'12, Beijing, China. 2012. After training, an intercept is added to represent the average prediction over the training set, and the shape functions are normalized to represent the deviation from the average prediction. This results in models that are easily interpreted simply by inspecting the intercept and the shape functions. See the sample below for an example of how to train a GAM model and inspect and interpret the results.
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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