探究不同阶数对4种线性回归影响

import numpy as np
from sklearn.linear_model import LinearRegression, RidgeCV, LassoCV, ElasticNetCV
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
from sklearn.exceptions import ConvergenceWarning
import matplotlib as mpl
import matplotlib.pyplot as plt
import warnings
def xss(y, y_hat): y = y.ravel() y_hat = y_hat.ravel() # Version 1 tss = ((y - np.average(y)) ** 2).sum() rss = ((y_hat - y) ** 2).sum() ess = ((y_hat - np.average(y)) ** 2).sum() r2 = 1 - rss / tss # print 'RSS:', rss, '\t ESS:', ess # print 'TSS:', tss, 'RSS + ESS = ', rss + ess # Version 2 # tss = np.var(y) # rss = np.average((y_hat - y) ** 2) # r2 = 1 - rss / tss corr_coef = np.corrcoef(y, y_hat)[0, 1] tss_list.append(tss) rss_list.append(rss) ess_list.append(ess) ess_rss_list.append(rss + ess) retur
  

 

  
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文章来源: maoli.blog.csdn.net，作者：刘润森！，版权归原作者所有，如需转载，请联系作者。

原文链接：maoli.blog.csdn.net/article/details/89457256