使用xgboost对pima-indians-diabetes.csv数据进行分类处理时，代码无法运行

问题相关代码
import xgboost# First XGBoost model for Pima Indians dataset
from 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

seed = 7
test_size = 0.33
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)

fit model no training data

model = XGBClassifier()
model.fit(X_train, y_train)

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))

运行结果及报错内容

请问问题的原因是什么？该如何解决

查看一下dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")里的路径，即在当前运行脚本目录下有没有此文件。可写成绝对路径试试。

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