- Download the code:
git clone https://github.com/qiyiping/gbdt.git - Run
maketo compile - Run the demo script in `test`:
./demo.sh
[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.
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;
...
};- 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)
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