可视化线性回归模型拟合python

# 导入所需的库
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

# 创建简单的数据集
np.random.seed(0)
X = np.random.rand(100, 1)
y = 2 + 3 * X + np.random.normal(0, 0.05, (100, 1))

# 将数据拆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=0)

# 创建线性回归模型并拟合训练数据
model = LinearRegression()
model.fit(X_train, y_train)

# 预测测试集的结果
y_pred = model.predict(X_test)

# 可视化线性回归模型的拟合
plt.scatter(X_test, y_test, color='blue', label='Actual Data')
plt.plot(X_test, y_pred, color='red', linewidth=2, label='Fitted Line')
plt.xlabel('X')
plt.ylabel('y')
plt.title('Linear Regression Model Fitting')
plt.legend()
plt.show()