拟合数据和预测的Python代码

```python import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error, r2_score # 生成数据 np.random.seed(0) X = np.random.rand(100, 1) y = 2 + 3 * X + np.random.rand(100, 1) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 拟合模型 model = LinearRegression() model.fit(X_train, y_train) # 预测 y_pred = model.predict(X_test) # 评估模型 mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) print("均方误差： ", mse) print("R2分数： ", r2) # 可视化 plt.scatter(X_test, y_test, color='blue', label='实际值') plt.plot(X_test, y_pred, color='red', linewidth=2, label='预测值') plt.xlabel('X') plt.ylabel('y') plt.legend() plt.show() ```