python评价回归模型指标：决定系数R2，相关系数R，均方误差MSE，均方根误差RMSE

#计算回归相关系数的方法   确定Ok

#相关系数第一种
def calc_corr(a,b):
    a_avg = sum(a)/len(a)
    b_avg = sum(b)/len(b)
    cov_ab = sum([(x - a_avg)*(y - b_avg) for x,y in zip(a, b)])
    sq = math.sqrt(sum([(x - a_avg)**2 for x in a])*sum([(x - b_avg)**2 for x in b]))
    corr_factor = cov_ab/sq
    return corr_factor

#相关系数第二种
import numpy as np
from astropy.units import Ybarn
import math
 
def computeCorrelation(X, Y):
    xBar = np.mean(X)
    yBar = np.mean(Y)
    SSR = 0
    varX = 0
    varY = 0
    for i in range(0 , len(X)):
        diffXXBar = X[i] - xBar
        diffYYBar = Y[i] - yBar
        SSR += (diffXXBar * diffYYBar)
        varX +=  diffXXBar**2
        varY += diffYYBar**2
    
    SST = math.sqrt(varX * varY)
    return SSR / SST
 

 #决定系数
 from sklearn.metrics import r2_score
 r2_score(y_true,y_pred)


#均方误差、均方根误差
from sklearn.merics import mean_squared_error
mes = mean_squared_error(y_true,y_pred)
rmse = np.sqrt(mse)

本文地址：https://blog.csdn.net/Ms__zhao/article/details/107352580