ValueError：发现样本数量不一致的输入变量：[143, 426]

Question

如何解决它引发的这个错误？ ValueError: 发现样本数量不一致的输入变量：[143, 426]

#split the data set into independent (X) and dependent (Y) data sets
X = df.iloc[:,2:31].values
Y = df.iloc[:,1].values

#split the data qet into 75% training and 25% testing
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)

#scale the data (feature scaling)
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_train = sc.fit_transform(X_test)

#Using Logistic Regression Algorithm to the Training Set

classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, Y_train)

以及X_train、Y_train的形状：

X_train.shape
(143, 29)
Y_train.shape
(426,)

错误信息： ValueError Traceback（最近一次调用最后一次） 在 （） 2 3 分类器 = LogisticRegression(random_state = 0) ----> 4 分类器.fit(X_train, Y_train) 5 #Using KNeighborsClassifier 邻居类的方法使用Nearest Neighbor算法 6

2 帧 /usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py in check_consistent_length(*arrays) 210 如果 len(uniques) > 1: 211 raise ValueError("发现输入变量的数量不一致" --> 212 " 样本: %r" % [int(l) for l in lengths]) 213 214

ValueError: 发现样本数量不一致的输入变量：[143, 426]

Answer 1

您在第 11 行有一个错误，您将分配给 X_train 而不是 X_test。看看下面更正后的代码。

#split the data set into independent (X) and dependent (Y) data sets
X = df.iloc[:,2:31].values
Y = df.iloc[:,1].values

#split the data qet into 75% training and 25% testing
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)

#scale the data (feature scaling)
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

#Using Logistic Regression Algorithm to the Training Set

classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, Y_train)

也不要在 X_test 上使用 fit_transform。您希望使用与 X_train 中计算的相同的均值和标准差。