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

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

data = np.array([
    [0.5, 1.4],
    [1.3, 1.9],
    [2.1, 3.0],
    [3.0, 3.7],
    [3.8, 4.2],
    [4.5, 5.1]
])

x_data = data[:, 0].reshape(-1, 1)
y_data = data[:, 1]

model = LinearRegression()

model.fit(x_data, y_data)

w = model.coef_[0]
b = model.intercept_

print(f"回归方程为: y = {w:.2f}x + {b:.2f}")

y_pred = model.predict(x_data)

plt.figure(figsize=(8, 6))
plt.scatter(x_data, y_data, color='blue', label='Data')
plt.plot(x_data, y_pred, color='red', label='Regression Line', linewidth=2)
plt.xlabel("X")
plt.ylabel("Y")
plt.legend()
plt.grid()
plt.show()