相关数据准备参考以下文章中的数据连接Python实践通过使用XGBoost中的尽早停止【Early Stopping】策略来避免过度拟合_Together_CZ的博客-CSDN博客

early_stopping_rounds : int, optionalActivates early stopping. Validation error needs to decrease atleast every  round(s) to continue training.Requires at least one item in evals.  If there's more than one,will use the last. Returns the model from the last iteration(not the best one). If early stopping occurs, the model willhave three additional fields: bst.best_score, bst.best_iterationand bst.best_ntree_limit.(Use bst.best_ntree_limit to get the correct value if num_parallel_treeand/or num_class appears in the parameters)

翻译过来就是，可以接受多个评估数据集，进行early_stop。

如果只有一个数据集，直接以该数据集进行评估，在达到指定的训练轮次之前，如果评估指标在该数据集上已经early_stopping_rounds没有提升，则停止训练，返回最后一轮迭代的模型，（并不是最好的一个），如果发生early_stop，会有额外三个参数： bst.best_score, bst.best_iteration and bst.best_ntree_limit，进行参考。如果是多个数据集，则以最后一个数据集的评估指标作为参考来评估是否要使用early_stop。

eval_metric，同样支持一个或者多个评估指标。如果eval_metric有多个，跟eval_set采用同样的逻辑，以最后一个metric作为参考。

本文提供两个参数，eval_set，eval_metric，供大家进行如上逻辑的测试：

import sysfrom numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# load data
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")
# split data into X and y
X = dataset[:,0:8]
Y = dataset[:,8]
# split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=7)
# fit model no training data
model = XGBClassifier(n_estimators=1000)
eval_set = [(X_train, y_train), (X_test, y_test)]
early_stopping_rounds = 100
model.fit(X_train, y_train, early_stopping_rounds=early_stopping_rounds, eval_metric=['logloss',"error"], eval_set=eval_set, verbose=True)
# make predictions for test data
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
# evaluate predictions
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))