Max*_*xPY 4 python machine-learning xgboost
我开始使用python xgboostbackage.有没有办法在每个训练时代获得训练和验证错误?我在文档中找不到一个
训练过一个简单的模型并获得输出:
[09:17:37] src/tree/updater_prune.cc:74:树修剪结束,1个根,124个额外节点,0个修剪节点,max_depth = 6
[0] eval-rmse:0.407474 train-rmse:0.346349 [09:17:37] src/tree/updater_prune.cc:74:树修剪结束,1根,116个额外节点,0个修剪节点,max_depth = 6
1 eval-rmse:0.410902 train-rmse:0.339925 [09:17:38] src/tree/updater_prune.cc:74:树修剪结束,1根,124个额外节点,0个修剪节点,max_depth = 6
[2] eval-rmse:0.413563 train-rmse:0.335941 [09:17:38] src/tree/updater_prune.cc:74:树修剪结束,1根,126个额外节点,0个修剪节点,max_depth = 6
[3] eval-rmse:0.418412 train-rmse:0.333071 [09:17:38] src/tree/updater_prune.cc:74:树修剪结束,1根,114个额外节点,0个修剪节点,max_depth = 6
但是,我需要在代码中传递这些eval-rmse和train-rmse进一步,或者至少绘制这些曲线.
保存中间结果的一种方法是将evals_result参数传递给xgb.train方法.
假设您已经创建了train一个evalXGB格式的矩阵,并params为XGBoost 初始化了一些参数(在我的例子中params = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }).
创建一个空字典
progress = dict()
创建一个关注列表,(我想你已经有了它,因为你正在打印train-rmse)
watchlist = [(train,'train-rmse'), (eval, 'eval-rmse')]
将这些传递给 xgb.train
bst = xgb.train(param, train, 10, watchlist, evals_result=progress)
在迭代结束时,progress字典将包含所需的训练/验证错误
> print progress
{'train-rmse': {'error': ['0.50000', ....]}, 'eval-rmse': { 'error': ['0.5000',....]}}
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