Wage数据集多项式回归

import pandas as pd
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
wage = pd.read_csv('https://liangdaima.com/static/data/statistics/Wage.csv')
X = wage['age'].values.reshape(-1, 1)
y = wage['wage'].values
for degree in range(1, 6):
    poly = PolynomialFeatures(degree, include_bias=False)
    X_poly = poly.fit_transform(X)
    model = LinearRegression()
    scores = -np.mean(cross_val_score(model, X_poly, y, cv=5, scoring='neg_mean_squared_error'))
    print(f'次数{degree}的交叉验证MSE: {scores:.2f}')