Linked Questions

38 votes
7 answers

Why is using squared error the standard when absolute error is more relevant to most problems? [duplicate]

I recognize that parts of this topic have been discussed on this forum. Some examples: Is minimizing squared error equivalent to minimizing absolute error? Why squared error is more popular than the ...
Ryan Volpi's user avatar
  • 1,838
1 vote
1 answer

Higher RMSE but lower MAE and RMLSE. Which model is better? [duplicate]

I am evaluating two machine learning models. The output is count data which has a range of 0 to 30, which most of the output values being small values. Large output values are rare. One model has ...
jkjsdf fod's user avatar
0 votes
1 answer

What to check in cross-validation - MAE or MSE? [duplicate]

When using cross-validation for model selection, should one look at MSE or MAE. I know that MSE and MAE are related but which is the more appropriate measure?
Raj's user avatar
  • 933
0 votes
1 answer

how to pick model, MAE or rMSE [duplicate]

Im building two models, (model1 and model2) I trained and tested them on the same test dataset, model1 will have mean absolute error (MAE) 10.3, rooted mean squared error (rMSE) 30.1 model2 will have ...
user2926523's user avatar
0 votes
1 answer

Why RMSE over MAE for matrix factorisation? [duplicate]

I have been trying to compare several matrix factorization algorithms and I've noticed that all the papers and libraries I've seen measure the Root Mean Square Error(RMSE) when intuitively I would ...
SARose's user avatar
  • 305
0 votes
2 answers

MAE or RMSE for my data? [duplicate]

I have been checking how each error metric works in the hope to find the best one for my data but it can be quite tricky actually. I have monthly time series data and I am running a SARIMA model to ...
Rods2292's user avatar
  • 371
34 votes
5 answers

Why do we take the square root of variance to create standard deviation?

Sorry if this is has been answered elsewhere, I haven't been able to find it. I am wondering why we take the square root, in particular, of variance to create the standard deviation? What is it about ...
Dave's user avatar
  • 2,441
10 votes
3 answers

Why there is square in MSE (mean squared error)?

Please forgive me for such a beginner question, since I'm learning stats . & machine learning. I'm trying to understand Mean Squared Error. I understand the "Mean Error", the Mean of Errors ...
rummykhan's user avatar
  • 203
7 votes
3 answers

L1 & L2 double role in Regularization and Cost functions?

I'm confused about the way L1 & L2 pop-up in what seem different roles in the same play: Regularization - penalty for the cost function, L1 as Lasso & L2 as Ridge Cost/Loss Function - L1 as ...
kama-shay's user avatar
8 votes
2 answers

Which metric to use for estimating accuracy of a climate model?

Let's assume I have 3 different climate models for a specific region that project the temperature. I also have the observations of that region for the same time frame(the real temperatures). My ...
Alex's user avatar
  • 81
7 votes
1 answer

F1 score, PR or ROC curve for regression

Due to my background as a pure biologist, I've been struggling with the comment acquired from a reviewer about the accuracy test used in my regression study. While I stick to MSE, MAE and R2 as the ...
Tofu King's user avatar
4 votes
2 answers

RMSE - where this evaluation metric came from?

Does anyone know where this metric came from ? Can someone bring article references or something like this? Im actually wondering if there's any mathematical concept or any way to demonstrate ...
Alvaro Silvino's user avatar
1 vote
2 answers

Should I convert the number after log transformation back to the original for calculating RMSE?

When I built the model, I applied the log transformation to all variables including the dependent variables. Now, I'm calculating the RMSE for the evaluation, and the result is in the log format, ...
Fangyuan's user avatar
  • 137
2 votes
1 answer

Mean Squared Error changes according to scale of value in machine learning regression problem

I am working on a machine learning regression problem and I have chosen the metrics Mean Absolute Error (MAE) and 'Mean Squared Error (MSE). I have 3 features and two of them have values in the range ...
user avatar
1 vote
3 answers

accuracy of a regression prediction model

I developed two prediction models using non-linear regression analysis to predict a set of values using sigmoidal and power functions. I was wondering how I can evaluate the accuracy of these ...
Samantha Peggy's user avatar

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