Uses of Class
weka.classifiers.trees.XGBoost.XGBoostParameter

Packages that use XGBoost.XGBoostParameter

Package	Description
weka.classifiers.trees

Uses of XGBoost.XGBoostParameter in weka.classifiers.trees

Fields in weka.classifiers.trees with annotations of type XGBoost.XGBoostParameter

Modifier and Type	Field	Description
protected float	XGBoost.m_Alpha	L1 regularisation term on weights.
protected float	XGBoost.m_BaseScore	Global bias.
protected XGBoost.BoosterType	XGBoost.m_BoosterType	The type of booster to use.
protected float	XGBoost.m_ColumnSampleByLevel	Subsample ratio of columns for each level.
protected float	XGBoost.m_ColumnSampleByNode	Subsample ratio of columns for each node (split).
protected float	XGBoost.m_ColumnSampleByTree	Subsample ratio of columns when constructing each tree.
protected float	XGBoost.m_Eta	The eta value (learning rate).
protected XGBoost.FeatureSelector	XGBoost.m_FeatureSelector	Feature selection and ordering method.
protected float	XGBoost.m_Gamma	The gamma value (minimum split loss).
protected XGBoost.GrowPolicy	XGBoost.m_GrowPolicy	Controls the way new nodes are added to the tree.
protected float	XGBoost.m_Lambda	L2 regularisation term on weights.
protected int	XGBoost.m_MaxBin	Maximum number of discrete bins to bucket continuous features.
protected float	XGBoost.m_MaxDeltaStep	Maximum delta step.
protected int	XGBoost.m_MaxDepth	The maximum depth of the tree.
protected int	XGBoost.m_MaxLeaves	Maximum number of nodes to be added.
protected float	XGBoost.m_MinChildWeight	The minimum child weight.
protected XGBoost.NormaliseType	XGBoost.m_NormaliseType	Type of normalisation algorithm.
protected int	XGBoost.m_NumberOfParallelTrees	The number of parallel trees constructed during each iteration.
protected int	XGBoost.m_NumberOfThreads	The number of threads to use.
protected XGBoost.Objective	XGBoost.m_Objective	The learning objective.
protected boolean	XGBoost.m_OneDrop	Whether to always drop at least one tree during dropout.
protected XGBoost.Predictor	XGBoost.m_Predictor	The type of predictor algorithm to use.
protected XGBoost.ProcessType	XGBoost.m_ProcessType	The type of boosting process to run.
protected float	XGBoost.m_RateDrop	Dropout rate.
protected XGBoost.SampleType	XGBoost.m_SampleType	Type of sampling algorithm.
protected float	XGBoost.m_ScalePositiveWeights	Scales the weights of positive instances by this factor.
protected int	XGBoost.m_Seed	The random number seed.
protected float	XGBoost.m_SkipDrop	Probability of skipping the dropout procedure during the boosting operation.
protected float	XGBoost.m_Subsample	Subsample ratio of the training instances.
protected int	XGBoost.m_TopK	The number of top features to select.
protected XGBoost.TreeMethod	XGBoost.m_TreeMethod	The tree construction algorithm.
protected float	XGBoost.m_TweedieVariancePower	Parameter that controls the variance of the Tweedie distribution.
protected XGBoost.Updater	XGBoost.m_Updater	Choice of algorithm to fit linear model.
protected XGBoost.Verbosity	XGBoost.m_Verbosity	Verbosity of printing messages.