作业二.自定义数据集使用scikit-learn中的包实现线性回归方法对其进行拟合

from sklearn.linear_model import LinearRegression

from sklearn.model_selection import train_test_split

from sklearn.metrics import mean_squared_error

import numpy as np

import matplotlib.pyplot as plt

np.random.seed(0)

加载自定义数据集

X = 2 * np.random.rand(100, 1)

y = 4 + 3 * X + np.random.randn(100, 1)

将数据集划分为训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

创建线性回归模型对象并拟合训练数据

model = LinearRegression()

model.fit(X_train, y_train)

使用训练好的模型对测试集进行预测

y_pred = model.predict(X_test)

计算预测误差

mse = mean_squared_error(y_test, y_pred)

print("均方误差：", mse)

plt.scatter(X_test, y_test, color='blue')

plt.plot(X_test, y_pred, color='red')

plt.show()