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GradientBoostedTrees

Timestamp:

03/03/17 11:41:43 (8 years ago)

Author:

gkronber

Message:

#2520

Location:

Files:

r13368	r14711
36	36	namespace HeuristicLab.Algorithms.DataAnalysis {
37	37	[Item("Gradient Boosted Trees (GBT)", "Gradient boosted trees algorithm. Friedman, J. \"Greedy Function Approximation: A Gradient Boosting Machine\", IMS 1999 Reitz Lecture.")]
38		[Storable~~Class~~("DA2F5BB9-19F3-42E8-A5D2-1CAAE12D1B6C")]
	38	[StorableType("DA2F5BB9-19F3-42E8-A5D2-1CAAE12D1B6C")]
39	39	[Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 125)]
40	40	public class GradientBoostedTreesAlgorithm : BasicAlgorithm {

r13368	r14711
30	30
31	31	namespace HeuristicLab.Algorithms.DataAnalysis {
32		[Storable~~Class~~("37C2B67D-B2A9-42CC-880B-AD5F61104FDD")]
	32	[StorableType("37C2B67D-B2A9-42CC-880B-AD5F61104FDD")]
33	33	[Item("Gradient boosted tree model", "")]
34	34	// this is essentially a collection of weighted regression models

r13368	r14711
28	28
29	29	namespace HeuristicLab.Algorithms.DataAnalysis {
30		[Storable~~Class~~("ECCC9F9F-E3B5-4D16-AB7F-B1221A5F7033")]
	30	[StorableType("ECCC9F9F-E3B5-4D16-AB7F-B1221A5F7033")]
31	31	// this class is used as a surrogate for persistence of an actual GBT model
32	32	// since the actual GBT model would be very large when persisted we only store all necessary information to

r13368	r14711
27	27
28	28	namespace HeuristicLab.Algorithms.DataAnalysis {
29		[Storable~~Class~~("B99E81E5-DC80-4552-8B40-0089662FFFEB")]
	29	[StorableType("B99E81E5-DC80-4552-8B40-0089662FFFEB")]
30	30	public sealed class GradientBoostedTreesSolution : RegressionSolution {
31	31	public new IGradientBoostedTreesModel Model {

r13368	r14711
29	29	namespace HeuristicLab.Algorithms.DataAnalysis {
30	30	// loss function for the weighted absolute error
31		[Storable~~Class~~("CDF5C3F1-0577-48FD-BA73-050231ADB015")]
	31	[StorableType("CDF5C3F1-0577-48FD-BA73-050231ADB015")]
32	32	[Item("Absolute error loss", "")]
33	33	public sealed class AbsoluteErrorLoss : Item, ILossFunction {

r13368	r14711
31	31	namespace HeuristicLab.Algorithms.DataAnalysis {
32	32	// Greedy Function Approximation: A Gradient Boosting Machine (page 9)
33		[Storable~~Class~~("89E2D6B3-2BDE-4FF2-98B5-236F4C3B3F69")]
	33	[StorableType("89E2D6B3-2BDE-4FF2-98B5-236F4C3B3F69")]
34	34	[Item("Logistic regression loss", "")]
35	35	public sealed class LogisticRegressionLoss : Item, ILossFunction {

r13368	r14711
32	32	// loss function for quantile regression
33	33	// Generalized Boosted Models - A Guide To The gbm Package, Greg Ridgeway, August 2007, page 11
34		[Storable~~Class~~("1D8A2DAE-4A77-4A37-8C03-7980A26B5E8B")]
	34	[StorableType("1D8A2DAE-4A77-4A37-8C03-7980A26B5E8B")]
35	35	[Item("QuantileRegressionloss", "Loss function for quantile regression")]
36	36	public sealed class QuantileRegressionLoss : ParameterizedNamedItem, ILossFunction {

r13368	r14711
31	31	namespace HeuristicLab.Algorithms.DataAnalysis {
32	32	// relative error loss is a special case of weighted absolute error loss with weights = (1/target)
33		[Storable~~Class~~("6C2C8370-D504-4CD0-8157-33024C0EC144")]
	33	[StorableType("6C2C8370-D504-4CD0-8157-33024C0EC144")]
34	34	[Item("Relative error loss", "")]
35	35	public sealed class RelativeErrorLoss : Item, ILossFunction {

r13368	r14711
29	29
30	30	namespace HeuristicLab.Algorithms.DataAnalysis {
31		[Storable~~Class~~("780DF644-DAD4-4A05-8C8D-9807628C72CB")]
	31	[StorableType("780DF644-DAD4-4A05-8C8D-9807628C72CB")]
32	32	[Item("Squared error loss", "")]
33	33	public sealed class SquaredErrorLoss : Item, ILossFunction {

r13368	r14711
32	32
33	33	namespace HeuristicLab.Algorithms.DataAnalysis {
34		[Storable~~Class~~("2B90568C-D6C1-48EE-86F4-62389FB60E0F")]
	34	[StorableType("2B90568C-D6C1-48EE-86F4-62389FB60E0F")]
35	35	[Item("RegressionTreeModel", "Represents a decision tree for regression.")]
36	36	public sealed class RegressionTreeModel : NamedItem, IRegressionModel {

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