sklearn线性回归训练报错：输入变量样本数不一致求助

问题场景 #

编写预测logS值的线性回归代码时，运行触发ValueError: Found input variables with inconsistent numbers of samples，调整test_size参数后报错样本数同步变化，但问题始终存在。

原代码 #

import pandas as pd
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

df=pd.read_csv('https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv')

X = df.drop('logS', axis=1)
y = df['logS']

X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

lr = LinearRegression()
lr.fit(X_train, y_train)

报错信息 #

Traceback (most recent call last):
  File "D:\Python\AI\test\main.py", line 14, in <module>
    lr.fit(X_train, y_train)
  File "D:\Python\AI\test\venv\lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
  File "D:\Python\AI\test\venv\lib\site-packages\sklearn\linear_model\_base.py", line 678, in fit
    X, y = self._validate_data(
  File "D:\Python\AI\test\venv\lib\site-packages\sklearn\base.py", line 622, in _validate_data
    X, y = check_X_y(X, y, **check_params)
  File "D:\Python\AI\test\venv\lib\site-packages\sklearn\utils\validation.py", line 1164, in check_X_y
    check_consistent_length(X, y)
  File "D:\Python\AI\test\venv\lib\site-packages\sklearn\utils\validation.py", line 407, in check_consistent_length
    raise ValueError(
ValueError: Found input variables with inconsistent numbers of samples: [915, 229]

问题原因 #

sklearn.model_selection.train_test_split函数的返回值顺序是固定的：训练集特征(X_train)、测试集特征(X_test)、训练集标签(y_train)、测试集标签(y_test)。

你错误地将返回值顺序写成了X_train, y_train, X_test, y_test，导致：

• X_train是训练集特征（样本数为总样本的80%，即915条）
• y_train被错误赋值为测试集特征（样本数为总样本的20%，即229条）
  两者样本数不匹配，因此调用lr.fit()时触发报错。

解决方法 #

修正train_test_split的变量接收顺序，改为正确的顺序即可：

修正后代码 #

import pandas as pd
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

df=pd.read_csv('https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv')

X = df.drop('logS', axis=1)
y = df['logS']

# 修正变量顺序：X_train, X_test, y_train, y_test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

lr = LinearRegression()
lr.fit(X_train, y_train)

# 可选：验证模型效果
y_pred = lr.predict(X_test)
print(f"R² Score: {r2_score(y_test, y_pred)}")
print(f"MSE: {mean_squared_error(y_test, y_pred)}")

内容的提问来源于stack exchange，提问作者ketchup