线性回归之决定系数（coefficientofdetermination）
1. Sum Of Squares Due To Error
同仇敌忾怎么读对于第i个观察点, 真实数据的Yi与估算出来的Yi-head的之间的差称为第i个residual, SSE 就是所有观察点的residual的和
记者节快乐
2. Total Sum Of Squares
交换英语3. Sum Of Squares Due To Regression
通过以上我们能得到以下关于他们三者的关系
去泰国要签证吗>祝福语四字成语决定系数: 判断回归⽅程的拟合程度
(coefficient of determination)决定系数也就是说: 通过回归⽅程得出的 dependent variable 有 number% 能被 independent variable 所解释. 判断拟合的程度
(Correlation coefficient) 相关系数 : 测试dependent variable 和 independent variable 他们之间的线性关系有多强. 也就是说, independent variable 产⽣变化时 dependent variable 的变化有多⼤.
可以反映是正相关还是负相关
注意此决定系数不能⽤来衡量⾮线性回归的拟合优度
Why Is It Impossible to Calculate a Valid R-squared for Nonlinear Regression?
法律之门
R-squared is bad on the underlying assumption that you are fitting a linear model. If you aren’t fitting a linear model, you shouldn’t u it. The reason why is actually very easy to understand.
For linear models, the sums of the squared errors always add up in a specific manner: SS Regression + SS Error = SS Total.
This ems quite logical. The variance that the regression model accounts for plus the error variance adds up to equal the total variance. Further, R-squared equals SS Regression / SS Total, which mathematically must produce a value between 0 and 100%.
In nonlinear regression, SS Regression + SS Error do not equal SS Total! This completely invalidates R-squared for nonlinear models, and it no longer has to be between 0 and 100%.
手机丢失怎么办更新：
For cas other than fitting by ordinary least squares, the R2 statistic can be calculated as above and may still be a uful measure. If fitting is by weighted least squares or generalized least squares, alternative versions of R2 can be calculated appropriate to tho statistical frameworks, while the "raw" R2 may still be uful if it is more easily interpreted. Values for R2 can be calculated for any type of predictive model, which need not have a statistical basis.
更新：
这篇回答中给了两个信息：
（1）线性回归的R⽅等于实际值与预测值的相关系数的平⽅
小学管理规程（2）randomForest is reporting variation explained as oppod to variance explained.
