struct Paras

#include "decision_tree_L2.hpp"

Overview

Decision Tree Train enviroment context struct.

Random Forest Train enviroment context struct.

Note that the structure are used in both host and kernel

As shown in the struct, there are 8 memebers.

1. cretiea is the algorithm to compute node impurity and information gain 0 : gini impurity(support) 1 : entropy (not support) 2 : Variance (not support)
2. min_leaf_size A node stops splitting when it receives the number of training instances
3. max_tree_depth is user input value in host, not transfer to device.
4. (for extension) maxBins determines the max splits of each feature. in current version, splits are read from configuration. The parameter is for splits generation extension
5. (for extension) min_samplecount_for_sample, for random forest and split generation sampling
6. (for extension) sample_percent_if_sample, for random forest and split generation sampling
7. (for extension) min_info_gain For entropy extension, a node stops splitting when its max gain <= this value

// fields

unsigned cretiea
unsigned max_tree_depth
unsigned min_leaf_size
float max_leaf_cat_per
float min_info_gain
int min_samplecount_for_sample
float sample_percent_if_sample
unsigned maxBins
float fea_fraction
ap_uint <64> seed