Gradient Boosting Regression Tree

Quick Start

• Download the code: git clone https://github.com/qiyiping/gbdt.git
• Run make to compile
• Run the demo script in `test`: ./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)