python-3.x - 发现输入变量样本数量不一致: [100, 300]

Question

我是这个领域的初学者，正在尝试根据逻辑回归对数据集进行建模。代码如下:

import numpy as np
from matplotlib import pyplot as plt
import pandas as pnd
from sklearn.preprocessing import Imputer, LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix

# Import the dataset
data_set = pnd.read_csv("/Users/Siddharth/PycharmProjects/Deep_Learning/Classification Template/Social_Network_Ads.csv")
X = data_set.iloc[:, [2,3]].values
Y = data_set.iloc[:, 4].values

# Splitting the set into training set and testing set
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25, random_state=0)

# Scaling the variables
scaler_x = StandardScaler()
x_train = scaler_x.fit_transform(x_train)
x_train = scaler_x.transform(x_test)

# Fitting Linear Regression to training data
classifier = LogisticRegression(random_state=0)
classifier.fit(x_train, y_train)

# Predicting the test set results
y_prediction = classifier.predict(x_test)

# Making the confusion matrix
conMat = confusion_matrix(y_true=y_test, y_pred=y_prediction)
print(conMat)

我收到的错误位于classifier.fit(x_train, y_train)中。错误是:

Traceback (most recent call last):
  File "/Users/Siddharth/PycharmProjects/Deep_Learning/Logistic_regression.py", line 24, in <module>
    classifier.fit(x_train, y_train)
  File "/usr/local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py", line 1173, in fit
    order="C")
  File "/usr/local/lib/python3.6/site-packages/sklearn/utils/validation.py", line 531, in check_X_y
    check_consistent_length(X, y)
  File "/usr/local/lib/python3.6/site-packages/sklearn/utils/validation.py", line 181, in check_consistent_length
    " samples: %r" % [int(l) for l in lengths])
ValueError: Found input variables with inconsistent numbers of samples: [100, 300]

我不知道为什么会发生这种情况。任何帮助将不胜感激。谢谢!!

Answer 1

你这里好像有错字。您可能想要:

x_test = scaler_x.transform(x_test)

而不是:x_train = scaler_x.transform(x_test)。简而言之，该错误基本上表明您的 x_train(实际上是 x_test)和 y_train 的大小不匹配。