Machine learning 机器学习&线性回归预测误差

Machine learning 机器学习&线性回归预测误差,machine-learning,Machine Learning,我试图通过简单的回归预测答案,但得到以下错误: '形状1151和603603未对齐:151尺寸1!=603暗 0' 这是我的密码 import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset`enter code here` dataset = pd.read_csv('pure_cotton.csv') X = dataset.iloc[:,7].values y

我试图通过简单的回归预测答案,但得到以下错误:

'形状1151和603603未对齐:151尺寸1!=603暗 0'

这是我的密码

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset`enter code here`
dataset = pd.read_csv('pure_cotton.csv')
X = dataset.iloc[:,7].values
y = dataset.iloc[:,10].values

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)


#fitting simple_linear_reg to training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit([X_train], [y_train])

#predicting the test results
y_pred = regressor.predict([X_test])

你应该做一些事情:

regressor.fit(np.expand_dims(X_train, 1), np.expand_dims(y_train, 1))


为了避免尺寸问题,第一个尺寸表示样本数量,第二个尺寸表示特征数量

您可以从X和y中删除.value。您不必将它们转换为numpy数组。但是,如果使用numpy数组,则可能必须使用np.reforme或np.expand_dims来更改数组的尺寸

你能给我看一下X_-train和y_-train的尺寸吗?X_-train和y_-train的尺寸是603,
regressor.predict(np.expand_dims(X_test, 1))