安装
SCIKIT-LEARN是一个基于Python/numpy/scipy的机器学习库 GBDT使用这段代码展示了一个简单的GBDT调用过程
import numpy as np from sklearn.ensemble import GradientBoostingRegressor gbdt=GradientBoostingRegressor( loss='ls' , learning_rate=0.1 , n_estimators=100 , subsample=1 , min_samples_split=2 , min_samples_leaf=1 , max_depth=3 , init=None , random_state=None , max_features=None , alpha=0.9 , verbose=0 , max_leaf_nodes=None , warm_start=False ) train_feat=np.genfromtxt("train_feat.txt",dtype=np.float32) train_id=np.genfromtxt("train_id.txt",dtype=np.float32) test_feat=np.genfromtxt("test_feat.txt",dtype=np.float32) test_id=np.genfromtxt("test_id.txt",dtype=np.float32) print train_feat.shape，rain_id.shape，est_feat.shape，est_id.shape gbdt.fit(train_feat,train_id) pred=gbdt.predict(test_feat) total_err=0 for i in range(pred.shape[0]): print pred[i],test_id[i] err=(pred[i]-test_id[i])/test_id[i] total_err+=err*err print total_err/pred.shape[0] train_id.txt示例 320 361 364 336 358 train_feat.txt示例 0.00598802 0.569231 0.647059 0.95122 -0.225434 0.837989 0.357258 -0.0030581 -0.383475 0.161677 0.743195 0.682353 0.960976 -0.0867052 0.780527 0.282945 0.149847 -0.0529661 0.113772 0.744379 0.541176 0.990244 -0.00578035 0.721468 0.43411 -0.318043 0.288136 0.0538922 0.608284 0.764706 0.95122 -0.248555 0.821229 0.848604 -0.0030581 0.239407 0.173653 0.866272 0.682353 0.95122 0.017341 0.704709 -0.0210016 -0.195719 0.150424 测试集我使用一样的数据 test_id.txt示例 320 361 364 336 358 test_feat.txt示例 0.00598802 0.569231 0.647059 0.95122 -0.225434 0.837989 0.357258 -0.0030581 -0.383475 0.161677 0.743195 0.682353 0.960976 -0.0867052 0.780527 0.282945 0.149847 -0.0529661 0.113772 0.744379 0.541176 0.990244 -0.00578035 0.721468 0.43411 -0.318043 0.288136 0.0538922 0.608284 0.764706 0.95122 -0.248555 0.821229 0.848604 -0.0030581 0.239407 0.173653 0.866272 0.682353 0.95122 0.017341 0.704709 -0.0210016 -0.195719 0.150424 测试结果与真值： 320.000817398 320.0 360.999648447 361.0 363.999282781 364.0 336.000234432 336.0 358.000016939 358.0