不适合线性回归的多项式度散点图点

import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline

# 生成一些非线性数据
np.random.seed(0)
x = np.linspace(-3, 3, 100)
y = x**2 + np.random.normal(0, 1, 100)

# 创建散点图
plt.scatter(x, y, color='blue', label='Data points')

# 使用多项式回归拟合数据
degree = 2  # 多项式度
model = make_pipeline(PolynomialFeatures(degree), LinearRegression())
model.fit(x[:, np.newaxis], y)

# 生成拟合曲线
x_fit = np.linspace(-3, 3, 100)
y_fit = model.predict(x_fit[:, np.newaxis])
plt.plot(x_fit, y_fit, color='red', label='Polynomial fit')

plt.legend()
plt.show()