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));
}
