• Gradient Boosting Regression Tree
    • Quick Start

** 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 #+BEGIN_SRC C++ 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; ... }; #+END_SRC ** 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)

Gbdt

Gbdt Info

⭐ Stars92
🔗 Source Codegithub.com
🕒 Last Updatea year ago
🕒 Created9 years ago
🐞 Open Issues1
➗ Star-Issue Ratio92
😎 Authorqiyiping