python - 为什么数据的变化不会改变情节线？

Question

我是机器学习的新手，我正在构建一个简单的线性回归模型。模型的变量如下:

X_train = [3, 5, 3, 4, 8, 7, 1, 10, 3, 2, 6, 6, 4, 9, 2, 1, 7, 5, 4, 8]
X_test = [2, 10, 4, 4, 10, 9, 10, 4, 5, 8]
Y_train = [56642, 66029, 64445, 61111, 113812, 91738, 46205, 121872, 60150, 39891, 81363, 93940, 57189, 54445, 105582, 43525, 39343, 98273, 67938, 56957]
Y_test = [37731, 122391, 57081, 63218, 116969, 109431, 112635, 55794, 83088, 101302]

这是我的代码:

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

# Importing the dataset
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].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 = 1/3, random_state = 0)


#fitting simple linear regression on training sets
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

#predicting the test set results
y_pred = np.array(regressor.predict(X_test))

这是我绘制图表的代码:

#visualizing data for the test set
plt.scatter(X_test, y_test, color = 'red')

#Method 1: Using regressor.predict(X_train)
plt.plot(X_train,regressor.predict(X_train), color = 'green')

#Method 2: Using regressor.predict(X_test)
plt.plot(X_test, regressor.predict(X_test), color='yellow')

plt.title('Experience v/s Salary')
plt.xlabel('Experience')
plt.ylabel('Salary')
plt.show()

这是我的图表图像:

为什么我对 plt.plot(X_train,regressor.predict(X_train), color = 'green') 和 plt.plot(X_test, regressor) 得到相同的回归线.predict(X_test), color='yellow') 尽管我得到了 regressor.predict(X_train) 和 regressor.predict(X_test) 的不同结果>?

Answer 1

线性回归模型实际上只是学习一条直线。对于任何输入，它会将输出映射到该直线上的一个点。你得到的是同一条线，无论输入如何，你总是会在那条线上得到一个点!

在拟合后查看 regressor.intercept_ 和 regressor.coef，这将显示 y 轴上的截距(从该图中可能约为 36000)，并且直线的梯度(大概是 10000？)。