逻辑回归（值误差解析）

Question

我实际上尝试使用逻辑回归算法来拟合训练数据集，但我得到了一个 ValueError 作为输出。

import pandas as pd
df = pd.read_csv('Social_Network_Ads (1).csv')
X = df[['User ID','Age','EstimatedSalary']]
Y = df['Purchased']
from sklearn.model_selection import train_test_split
X_train,Y_train,X_test,Y_test = train_test_split(X,Y,test_size=0.3,random_state=0)
from sklearn.linear_model import LogisticRegression
logistic_regression = LogisticRegression()
logistic_regression.fit(X_train,Y_train)

ValueError                                Traceback (most recent call last)
Cell In[114], line 2
      1 logistic_regression = LogisticRegression()
----> 2 logistic_regression.fit(X_train,y_train)

ValueError: y should be a 1d array, got an array of shape (120, 3) instead.

在尝试使用逻辑回归算法拟合训练数据集时，我得到了如上所述的 ValueError。如果我能得到它的分辨率，那就太好了。

Answer 1

train_test_split

返回值的解包顺序不正确。线

X_train,Y_train,X_test,Y_test = train_test_split(...)

应该是

X_train, X_test, Y_train, Y_test = train_test_split(...)

您可以通过阅读文档找到这一点。