ML::DT::DecisionTreeParams Struct Reference

#include <decisiontree.hpp>

Collaboration diagram for ML::DT::DecisionTreeParams:

Public Attributes
int	max_depth
int	max_leaves
float	max_features
int	max_n_bins
int	min_samples_leaf
int	min_samples_split
CRITERION	split_criterion
float	min_impurity_decrease = 0.0f
int	max_batch_size

Member Data Documentation

max_batch_size

int ML::DT::DecisionTreeParams::max_batch_size

Maximum number of nodes that can be processed in a given batch. This is used only for batched-level algo

max_depth

int ML::DT::DecisionTreeParams::max_depth

Maximum tree depth. Unlimited (e.g., until leaves are pure), If -1.

max_features

float ML::DT::DecisionTreeParams::max_features

Ratio of number of features (columns) to consider per node split.

max_leaves

int ML::DT::DecisionTreeParams::max_leaves

Maximum leaf nodes per tree. Soft constraint. Unlimited, If -1.

max_n_bins

int ML::DT::DecisionTreeParams::max_n_bins

maximum number of bins used by the split algorithm per feature.

min_impurity_decrease

float ML::DT::DecisionTreeParams::min_impurity_decrease = 0.0f

Minimum impurity decrease required for splitting a node. If the impurity decrease is below this value, node is leafed out. Default is 0.0

min_samples_leaf

int ML::DT::DecisionTreeParams::min_samples_leaf

The minimum number of samples (rows) in each leaf node.

min_samples_split

int ML::DT::DecisionTreeParams::min_samples_split

The minimum number of samples (rows) needed to split an internal node.

split_criterion

CRITERION ML::DT::DecisionTreeParams::split_criterion

Node split criterion. GINI and Entropy for classification, MSE for regression.

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

• include/cuml/tree/decisiontree.hpp