public static RandomForest Train(IDataTable table, int b = 100,
DecisionTreeTrainer.Config config = null)
{
config = config ?? new DecisionTreeTrainer.Config();
// set the feature bag count as the square root of the total number of features
if (!config.FeatureBagCount.HasValue)
{
config.FeatureBagCount =
Convert.ToInt32(
Math.Round(Math.Sqrt(table.Columns.Sum(c => c.IsContinuous ? 1 : c.NumDistinct))));
}
// repeatedly train a decision tree on a bagged subset of features
var ret = new List <DecisionTree>();
for (var i = 0; i < b; i++)
{
var baggedTree = table.Bag();
ret.Add(DecisionTreeTrainer.Train(baggedTree, config));
}
return(new RandomForest {
Forest = ret.ToArray()
});
}
