training parameters for regression tree More...

#include <param.h>

Inheritance diagram for xgboost::tree::TrainParam:

Public Types
enum	TreeGrowPolicy { kDepthWise = 0 , kLossGuide = 1 }

enum	SamplingMethod { kUniform = 0 , kGradientBased = 1 }

Public Member Functions
	DMLC_DECLARE_PARAMETER (TrainParam)

bool	NeedPrune (double loss_chg, int depth) const
	given the loss change, whether we need to invoke pruning

bst_node_t	MaxNodes () const

Public Member Functions inherited from xgboost::XGBoostParameter< TrainParam >
Args	UpdateAllowUnknown (Container const &kwargs)

bool	GetInitialised () const

Static Public Member Functions
static constexpr double	DftSparseThreshold ()

Data Fields
float	learning_rate

float	min_split_loss

int	max_depth

int	max_leaves

int	max_bin

int	grow_policy

uint32_t	max_cat_to_onehot {4}

bst_bin_t	max_cat_threshold {64}

float	min_child_weight

float	reg_lambda

float	reg_alpha

float	max_delta_step

float	subsample

int	sampling_method

float	colsample_bynode

float	colsample_bylevel

float	colsample_bytree

float	sketch_ratio

bool	cache_opt

bool	refresh_leaf

std::vector< int >	monotone_constraints

std::string	interaction_constraints

double	sparse_threshold {DftSparseThreshold()}

Additional Inherited Members
Protected Attributes inherited from xgboost::XGBoostParameter< TrainParam >
bool	initialised_

Detailed Description

training parameters for regression tree

The documentation for this struct was generated from the following file:

Public Types