问如何选择与PolynomialFeatures最合适的学位标准？

EN

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm                                                                                                                          

import statsmodels.formula.api as smf                                                                                                                 

import statsmodels.tsa.api as smt
import random
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline


y = [5*np.random.normal() for j in range(50)] + [30 + 5 * np.random.normal() for j in range(50)] +  [50 + 5 * np.random.normal() for j in range(50)] +  [20 + 5 * np.random.normal() for j in range(50)]
X = [x for x in range(len(y))]
X = np.reshape(X, (len(X), 1))

model = LinearRegression()
model.fit(X, y)
trend = model.predict(X)

model = make_pipeline(PolynomialFeatures(2), Ridge())
model.fit(X, y)
quadratic = model.predict(X)

fig = plt.figure(1, figsize=(15, 9))
ax = fig.add_subplot(111)
ax.plot(trend, label="Linear Trend")
ax.plot(quadratic, label="Quadratic Trend")
ax.plot(X, y, label='Time Series')
ax.legend()
plt.show()