Skip to content

kingzqwang/gbdt

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

81 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Gradient Boosting Regression Tree

Quick Start

  • Download the code: git clone https://github.com/qiyiping/gbdt.git
  • Run make to compile (boost is required)
  • Run the demo script: ./demo.sh

Data Format

[InitalGuess] Label Weight Index0:Value0 Index1:Value1 ..

Each line contains an instance and is ended by a ‘\n’ character. Inital guess is optional. For two-class classification, Label is -1 or 1. For regression, Label is the target value, which can be any real number. Feature Index starts from 0. Feature Value can be any real number.

Training Configuration

class Configure {
 public:
  size_t number_of_feature;      // number of features
  size_t max_depth;              // max depth for each tree
  size_t iterations;             // number of trees in gbdt
  double shrinkage;               // shrinkage parameter
  double feature_sample_ratio;    // portion of features to be splited
  double data_sample_ratio;       // portion of data to be fitted in each iteration
  size_t min_leaf_size;          // min number of nodes in leaf

  Loss loss;                     // loss type

  bool debug;                    // show debug info?

  double *feature_costs;         // mannually set feature costs in order to tune the model
  bool enable_feature_tunning;   // when set true, `feature_costs' is used to tune the model

  bool enable_initial_guess;
...
};

Reference

  • Friedman, J. H. “Greedy Function Approximation: A Gradient Boosting Machine.” (February 1999)
  • Friedman, J. H. “Stochastic Gradient Boosting.” (March 1999)
  • Jerry Ye, et al. (2009). Stochastic gradient boosted distributed decision trees. (Distributed implementation)

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published