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DTrees::Split Class Reference
Data Fields

DTrees::Split Class Reference

The class represents split in a decision tree. More...

#include <ml.hpp>

Data Fields

int varIdx
 Index of variable on which the split is created.
bool inversed
 If true, then the inverse split rule is used (i.e.
float quality
 The split quality, a positive number. It is used to choose the best split.
int next
 Index of the next split in the list of splits for the node.
float c
 The threshold value in case of split on an ordered variable.
int subsetOfs
 Offset of the bitset used by the split on a categorical variable.

Detailed Description

The class represents split in a decision tree.

Definition at line 1051 of file ml.hpp.

Field Documentation

float c

The threshold value in case of split on an ordered variable.

The rule is: 

 {.none}
                      if var_value < c
                        then next_node <- left
                        else next_node <- right

Definition at line 1060 of file ml.hpp.

bool inversed

If true, then the inverse split rule is used (i.e.

left and right branches are exchanged in the rule expressions below).

Definition at line 1056 of file ml.hpp.

int next

Index of the next split in the list of splits for the node.

Definition at line 1059 of file ml.hpp.

float quality

The split quality, a positive number. It is used to choose the best split.

Definition at line 1058 of file ml.hpp.

int subsetOfs

Offset of the bitset used by the split on a categorical variable.

The rule is: 

 {.none}
                            if bitset[var_value] == 1
                                then next_node <- left
                                else next_node <- right

Definition at line 1067 of file ml.hpp.

int varIdx

Index of variable on which the split is created.

Definition at line 1055 of file ml.hpp.

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