问题描述
我正在使用XGBoost算法进行回归,
I am running a regression using the XGBoost Algorithm as,
clf = XGBRegressor(eval_set = [(X_train, y_train), (X_val, y_val)],
early_stopping_rounds = 10,
n_estimators = 10,
verbose = 50)
clf.fit(X_train, y_train, verbose=False)
print("Best Iteration: {}".format(clf.booster().best_iteration))
它可以正确训练自己,但是打印功能会引发以下错误,
It correctly trains itself, but the print function raises the following error,
TypeError: 'str' object is not callable
如何获取模型的最佳 迭代次数?
此外,如何打印每个 一轮中的培训 错误?
Furthermore, how can I print the training error of each round?
推荐答案
对于您的TypeError:使用 get_booster()而不是booster()
For your TypeError: use get_booster() instead of booster()
print("Best Iteration: {}".format(clf.get_booster().best_iteration))
要在预测时使用最佳迭代次数,请使用一个名为ntree_limit
的参数,该参数指定要使用的增强器数量.从训练过程中生成的值是best_ntree_limit
,可以在以下事项中训练模型后调用该值:clg.get_booster().best_ntree_limit
.更具体地说,当您进行预测时,请使用:
To use the number of the best iteration when you predict, you have a parameter called ntree_limit
which specify the number of boosters to use. And the value generated from the training process is best_ntree_limit
which can be called after training your model in the following matter: clg.get_booster().best_ntree_limit
. More specifically when you predict, use:
best_iteration = clg.get_booster().best_ntree_limit
predict(data, ntree_limit=best_iteration)
如果您在.fit()命令
clf.fit(X_train, y_train,
eval_set = [(X_train, y_train), (X_val, y_val)],
eval_metric = 'rmse',
early_stopping_rounds = 10, verbose=True)
注意::Early_stopping_rounds参数应该在.fit()
命令中,而不是在XGBRegressor()
实例中.
NOTE: early_stopping_rounds parameter should be in the .fit()
command not in the XGBRegressor()
instantiation.
另一个注意事项: XGBRegressor()
中的verbose = 50
是多余的. verbose
变量应该在您的.fit()
函数中,并且为True或False.对于verbose = True所做的操作,在此处阅读在详细部分下.这直接影响到您的第三个问题.
Another NOTE: verbose = 50
in XGBRegressor()
is redundant. The verbose
variable should be in your .fit()
function and is True or False. For what the verbose=True do, read here under the verbose section. It is directly affects your 3rd question.
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