LightGbmRankingModelParameters Class

Definition

Namespace:

Microsoft.ML.Trainers.LightGbm

Assembly:

Microsoft.ML.LightGbm.dll

Package:

Microsoft.ML.LightGbm v3.0.1

Model parameters for LightGbmRankingTrainer.

public sealed class LightGbmRankingModelParameters : Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree

type LightGbmRankingModelParameters = class
    inherit TreeEnsembleModelParametersBasedOnRegressionTree

Public NotInheritable Class LightGbmRankingModelParameters
Inherits TreeEnsembleModelParametersBasedOnRegressionTree

Inheritance

Object

ModelParametersBase<Single>

TreeEnsembleModelParameters

TreeEnsembleModelParametersBasedOnRegressionTree

LightGbmRankingModelParameters

Properties

TrainedTreeEnsemble

An ensemble of trees exposed to users. It is a wrapper on the internalMicrosoft.ML.Trainers.FastTree.InternalTreeEnsemble in TreeEnsemble<T>. (Inherited from TreeEnsembleModelParametersBasedOnRegressionTree)

Methods

GetFeatureWeights(VBuffer<Single>)

Get the cumulative split gains for each feature across all trees. (Inherited from TreeEnsembleModelParameters)

Explicit Interface Implementations

ICalculateFeatureContribution.FeatureContributionCalculator

Used to determine the contribution of each feature to the score of an example by FeatureContributionCalculatingTransformer. The calculation of feature contribution essentially consists in determining which splits in the tree have the most impact on the final score and assigning the value of the impact to the features determining the split. More precisely, the contribution of a feature is equal to the change in score produced by exploring the opposite sub-tree every time a decision node for the given feature is encountered. Consider a simple case with a single decision tree that has a decision node for the binary feature F1. Given an example that has feature F1 equal to true, we can calculate the score it would have obtained if we chose the subtree corresponding to the feature F1 being equal to false while keeping the other features constant. The contribution of feature F1 for the given example is the difference between the original score and the score obtained by taking the opposite decision at the node corresponding to feature F1. This algorithm extends naturally to models with many decision trees. (Inherited from TreeEnsembleModelParameters)

ICanSaveModel.Save(ModelSaveContext)

(Inherited from ModelParametersBase<TOutput>)