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
