Classification and Regression Trees

Running the program

cd cart
make

Random Forest

Running the program

cd random_forest
make

Required Parameters

• -t ../data/train_file
  • input train file to matrix
• -s ../data/result_file
  • output predict labels to result file

Optional Parameters

• -p 16
  • use 16 threads in a thread pool to train trees
• -n 1000
  • use 1000 trees in the forest
• -f 30
  • use a subset of 30 features for each tree

Gradient Boosting Regression Tree

Running the program

cd gradient_boosting
./run.sh

Required Parameters

• train_path = "examples/train_reg.txt"
• eval[test] = "examples/test_reg.txt"
• test_reg = "examples/test_reg.txt"

Optional Parameters

• booster = gbtree

• objective = reg:linear

• eta = 1.0

• gamma = 1.0

• min_child_weight = 1

• max_depth = 3

• num_round = 2

• save_period = 0