import xgboost as xgb
import numpy as np
dtrain = xgb.DMatrix('./demo/data/agaricus.txt.train')
dtest = xgb.DMatrix('./demo/data/agaricus.txt.test')

param = {
    'silent': 1,
    'objective': 'binary:logistic',
    'booster': 'gblinear', # 选择线性模型
    'alpha': 0.0001, # L1 正则系数
    'lambda': 1 # L2 正则系数
}

watchlist = [(dtest, 'eval'), (dtrain, 'train')]

num_round = 4

bst = xgb.train(param, dtrain, num_round, watchlist)

[0]    eval-error:0.114215    train-error:0.104714
[1]    eval-error:0.116698    train-error:0.106249
[2]    eval-error:0.120422    train-error:0.10671
[3]    eval-error:0.121043    train-error:0.107017

pred = bst.predict(dtest)

label = dtest.get_label()

print('Error = %f' % (sum([1 for i in range(len(pred)) if ((pred[i] > 0.5) != label[i])]) / float(len(label))))

Error = 0.121043

# print('Error = %f' % (np.sum((pred > 0.5) != label)) / float(len(label))) # 这种先变成一个 str，再执行 /

print('Error = %f' % ((np.sum((pred > 0.5) != label)) / float(len(label))))

Error = 0.121043

这个文件学习了创建线性模型和 L1 L2 正则