private void btnTrain_Click(object sender, EventArgs e) { if (IsTrainingInProgress) { ConfirmExecutionInterruption(); } else { ISupervisedTrainer trainer = new RandomForestTrainer(TreeCount, BandCountPerSplit, MaxTreeHeight, MinNodeSize, BootstrappingRatio, IsParallel); TryTrainingAsync(trainer); } }
/// <summary> /// Random forests are built on a bagged collection of features to try to capture the most salient points of the training data without overfitting /// </summary> /// <param name="data">The training data</param> /// <param name="b">The number of trees in the forest</param> /// <returns>A model that can be used for classification</returns> public static RandomForest TrainRandomForest(this IDataTable data, int b = 100) { return(RandomForestTrainer.Train(data, b)); }