RMSE between original and predicted values.
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Hi,
If I have thousand samples of my signal in a vector form like 1*1000, and I will predict my signal at each iteration that results into 1*1000 also. Then In this case, how will I find the RMSE of my model?
Many Thanks
采纳的回答
Star Strider
2020-1-18
0 个投票
8 个评论
MAT-Magic
2020-1-18
Star Strider
2020-1-18
As always, my pleasure!
Star Strider
2020-1-19
It is correct, however you can write it much more simply:
v1 = [0.3 0.6 0.9];
v2 = [0.8 0.9 0.7];
RMSE = sqrt(mean((v1-v2).^2))
producing:
RMSE =
0.355902608401044
Remembering that ‘RMSE’ means the ‘root of the mean of the squares’.
MAT-Magic
2020-1-20
Star Strider
2020-1-20
The RMSE calculation remains the same. You need to take the diferences, square them, accumulate them, take the mean, and the the square root of that.
MAT-Magic
2020-1-20
Star Strider
2020-1-20
As always, my pleasure!
更多回答（1 个）
Image Analyst
2020-1-18
Without any other information, the maximum likelihood prediction for every element would be the mean of the entire signal. But it seems you'd rather have the rms, so you'd have
RMSE = rms(yourVector)
predictionVector = RMSE * ones(length(yourVector));
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