解决ValueError: bad input shape (xxxx, x)

下面看一段机器学习代码：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score
iris=datasets.load_iris()
x_train,y_train,x_test,y_test = train_test_split(iris.data,iris.target,test_size=0.4,random_state=0)
clf=GaussianNB()
clf.fit(x_train,y_train)

使用高斯朴素贝叶斯模型对鸢尾花数据集进行训练，但运行时出现以下问题：

查看错误原因，提示为值错误，这个错误发生在模型训练位置，并且模型训练语句没有错误，那么就是传给模型训练的数据发生了错误（x_train,y_train）！！！我们返回去检查这两个数据集，发现使用train_test_split在分割数据集时，我们将数据集顺序写错了，正确的数据集顺序为：x_train,x_test,y_train,y_test，正是由于这个正确的顺序，才能分割正确的数据集与测试集，大部分人认为这个数据集没有顺序，下面看一下train_test_split的常规用法：

X_train,X_test, y_train, y_test =sklearn.model_selection.train_test_split(train_data,train_target,test_size=0.4, random_state=0)

# train_data：所要划分的样本特征集

# train_target：所要划分的样本结果

# test_size：样本占比，如果是整数的话就是样本的数量

# random_state：是随机数的种子。

因此千万不要忽略训练集与测试集的顺序！

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