Standardisation and measurement criteria
1 次查看(过去 30 天)
显示 更早的评论
Hi everyone,
Can anyone help me with this problem: I have used zscore to standardise my linear model with 3 predictor variables. The question is, how do I interpret now my MAE,MSE, RMSE? As e.g. the MAE normally would give me an exact number, now I only get a 0.04 number... how do I inpret it?
Thank you!
0 个评论
采纳的回答
dpb
2014-7-27
编辑:dpb
2014-7-27
>> x=1:10;x=x';y=rand(size(x)); % some toy data
>> [b,~,~,~,stats]=regress(y,[ones(size(x)) x]); % not standardized
>> [bz,~,~,~,zstats]=regress(y,[ones(size(x)) zscore(x)]); % standardize x
>> [stats' zstats']
ans =
0.0100 0.0100
0.0808 0.0808
0.7834 0.7834
0.0643 0.0643
They're the same...just must remember to standardize x before evaluating the regression.
Now, add in standarization of y, too...
>> [bzy,~,~,~,zystats]=regress(zscore(y),[ones(size(x)) zscore(x)]);
>> [stats' zstats' zystats']
ans =
0.0100 0.0100 0.0100
0.0808 0.0808 0.0808
0.7834 0.7834 0.7834
0.0643 0.0643 1.1137
>>
Now the variance of the residuals is different because the scale factor for y is changed--with the rand() y used here, it's actually larger numerical since std(y)<1.
The upshot is, those statistics whose values are dependent upon the magnitude are scaled per the computation of the statistic whereas those that are either not scaled depending on which version of standardization used (x or x and y) or are invariant to the magnitude of the variables aren't.
0 个评论
更多回答(0 个)
另请参阅
类别
在 Help Center 和 File Exchange 中查找有关 Smoothing 的更多信息
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!