Questions tagged [mae]
The Mean Absolute Error (MAE) is a point forecast accuracy measure. In the forecasting literature, Mean Absolute Deviation (MAD) is used interchangeably.
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Comparison of MAE and Mean to illustrate the error magnitude
I have predicted a time series with positive, zero and negative values. As error measurement I used the Mean Absolute Error (MAE).
In order to give the reader of my paper a better understanding ...
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If predicted responses has an RMSE/MAE, how often will a prediction fall below the error? [closed]
As the title asks, If I for instance has a regression based model with an error rate, either in terms of RMSE (could be interpreted as a standard deviation?) or MAE, how often will the predicted value ...
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1answer
18 views
Learning Curves using different models
I am running repeated K-fold Cross-Validation on my dataset using different models. My problem is a regression problem and I am counting on the error metric MAE. I do know that some models may behave ...
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1answer
25 views
Dividing the MAE by the average of the values
I would like to parse the MAE (Mean Absolute Error) to a percentage value. I know there is the MAPE (Mean Absolute Percentage Error), however it has some drawbacks as going to infinity if one of my ...
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986 views
Can RMSE and MAE have the same value?
I am implementing cross validation and calculating error metrics such as RMSE, $R^2$, MAE, MSE, etc.
Can RMSE and MAE have the same value?
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1answer
48 views
RSME, MAE and prediction interval [closed]
Could someone please clarify, whether it is appropriate to define a prediction interval or an equivalent for an RMSE and MAE measure. If so, could you please suggest how such an interval is defined.
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50 views
MSE Intuition and Interpretation
I've got a very small question. Say I'm making a linear regression model. When I test the model with a testing set, I get an MSE of 4.31 (arbitrary). What do I interpret from this? As in, what does ...
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60 views
Error distribution for Huber Regression
For linear regression there's an assumption that error terms come from normal distribution. so that $Y = aX + b + \epsilon$, where $\epsilon$ has normal distribution with mean zero and certain ...
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38 views
How does MAE as objective function impact gradient boosting training compared to MSE?
I have a regression problem where I want to minimize MAE as a business metric.
I'm using LightGBM. I initially used the default objective function for regression ...
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1answer
80 views
a measure for MAE (of a regression)
I'm running a grid search, in order to fine-tune a NN hyper parameters.
the question is:
the MAE values I get from the trainings are too close.
since I have the statistical attributes of the target ...
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3answers
168 views
Standardised mean absolute error (SMAE) and how to calculate it?
I am using the mean absolute error
mean(abs(obs - pred))
as one of the measures assessing the fit of my model. I would also like to have a standardised measure ...
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1answer
97 views
What is the best point forecast for gamma distributed data?
I believe that the values I am forecasting are gamma distributed with shape $k>0$ and scale $\theta>0$. I need a point forecast (i.e., a one-number summary) that minimizes the expected error. ...
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104 views
What is the best point forecast for lognormally distributed data?
I believe that the values I am forecasting are lognormally distributed with log-mean $\mu$ and log-variance $\sigma^2$. I need a point forecast (i.e., a one-number summary) that minimizes the expected ...
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1answer
316 views
calculating overall error in k-fold cross validation
when using k-fold cross validation i thought the overall error was equal to the mean of errors of each fold. the error being anything from MAE and RMSE to NDCG,F-measure, precision and recall. however ...
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438 views
Mean Absolute Error in Random Forest Regression
I am new to the whole ML scene and am trying to resolve the Allstate Kaggle challenge to get a better feeling for the Random Forest Regression technique.
The challenge is evaluated based on the MAE ...
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32 views
How to best evaluate a cross validation of a logistic regression using cbind
I ran a logistic GLMM using cbind for the response:
...
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27 views
Calculate the Confidence Interval for the Error of Model
I am not sure I am thinking about my problem the right way, so I am looking for the right approach.
I have a data set that, for the sake of argument, has a mean of 1 and a standard deviation of $\...
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1answer
33 views
What statistical test do I need for comparing forecasted data with actual data?
I’m currently completing my dissertation and need to compare forecasted wave height to the actual wave height.
However I am unsure what statistical test to use.
Thanks,
Jess
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1answer
138 views
Does normalisation affect the values of Mean Squared Error, Mean Absolute Percentage Error etc.?
I have a large amount of data, divided into folders and files. Each file has 56 features/columns and around 10,000 rows. The data is normalised (values between -1 and 1) and some of the features have ...
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Why does minimizing the MAE lead to forecasting the median and not the mean?
From the Forecasting: Principles and Practice textbook by Rob J Hyndman and George Athanasopoulos, specifically the section on accuracy measurement:
A forecast method that minimizes the MAE will ...
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1answer
247 views
MSE or MAE absolute and relative performance
I'm comparing different ARMA-GARCH specification out-of-sample in order to understand whether the more "parsimonious" models prescribed by BIC do not perform more poorly than the more "expensive" ones ...
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1answer
587 views
MAE and Precision for Collaborative Filtering Recommender Systems
I have got a question concerning Recommender Systems and Evaluation Metrics. I tested a few collaborative filtering recommendation algorithms on dataset containing amazon ratings.
Here you can see MAE ...
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1answer
5k views
Mean Absolute Error (MAE) derivative
$MAE=|y_{pred} - y_{true}|$
$\dfrac{dMAE}{dy_{pred}} = ?$
I'm trying to understand how MAE works as a loss function in neural networks using backpropogation. I know it can be used directly in some ...
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1answer
113 views
improve performance in a regression methodology
I'm performing my own nonparametric regression in matlab and I'm wondering to switch one nuance of my methodology:
Methodology:
split Training/test set (i.e. 80/20 %)
search the best gamma ...
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Why does the MAE of my estimation improve substantially when I divide it by 10 (in a very noisy dataset)?
I have an extremely noisy dataset, and I try to minimize the MAE. Apparently, when my linear regression makes its prediction - it has something like -1000 MAE (when compared to the MAE of an all-zero ...
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1answer
1k views
Can RMSE be smaller than MAE?
Generally speaking, can RMSE be smaller than MAE?
I am calculating RMSE and MAE for my results. In two out of five methods, the RMSE is smaller than MAE. Note that I am using the same data, the same ...
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Is there an analog for the coefficient of determination with absolute error?
Typically to measure the predictive power of a model, R^2 is more useful than RMSE because a RMSE score isn't very meaningful without a basis of comparison. If you are using mean absolute error (MAE) ...
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1answer
621 views
MAE has gotten worse but RMSE is better, how should I interpret it?
I am doing some testing in recommender systems with extended epinions dataset, I implemented two models, model A give me RMSE of 0.5387 and MAE of 0.3111 and model B gave me RMSE of 0.5121 and MAE of ...
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1answer
4k views
L1 (MAE) vs L2 (MSE) when data is normalized between 0 and 1
In most of the literature, it is emphasized that the L2 norm (MSE) gives a higher error when dealing with outliers compared to the L1 norm (MAE).
But what happens when we normalize the data between 0 ...
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1answer
965 views
Can MAPE Decrease while StdDev Increases?
Can a decrease in mean absolute percentage error (MAPE) be correlated with an increase in standard deviation of the error? Is that counter-intuitive? What about MAPE vs. mean absolute error (MAE)? I'm ...
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1answer
813 views
Mean Absolute Error Cost Function in Logistic Regression
Is there any reasons not to use MAE as cost function in logistic regression?
Yes, I know about problems with optimization, I know that this estimation won't be max-likelihood estimation.
But is there ...
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1answer
221 views
Best imputation techniques (error test)
actually my study is about to choose best imputation techniques. Im comparing all the techniques using Root Mean Square Error (RMSE), Mean Aabsolute Error (MAE) and correlation (R). My problem is ...
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861 views
What is the point of Root Mean Absolute Error, RMAE, when evaluating forecasting errors?
RMAE is defined as the square root of the Mean Absolute Error (MAE). Presumably this is by analogy to Root Mean Square Error (RMSE) being defined as the square root of Mean Square Error (MSE).
But ...
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1answer
50 views
Error measure that take the time of prediction into account?
I am doing a machine learning project where I'm attempting to predict the arrival times of buses equipped with GPS at bus stops, and I am very new to statistics/data analysis. I am looking for an ...
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1answer
893 views
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 ...
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1answer
318 views
Relatively low MAE, but also low R-squared? Why
I am testing forecasts against against realized values with a number of observations of 4000.
When I calculate the mean MAE, its relatively low. Around 10% deviation to the original variable.
...
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2answers
784 views
Using GARCH rolling forecast in R to calculate MAE?
My intention is to calculate the MAE for different (G)ARCH-models (comparing the one-step-ahead forecast for $\sigma$ with the absolute return that day).
The formula for MAE is actually clear, but I'...
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476 views
Large-scale MAE regression in R
I have a large, sparse dgCMatrix matrix in R:
~200,000 rows
~150,000 columns
~1,000,000,000 non-zero entries
R code to generate the matrix:
...
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239 views
How to check if specific MAE and MSE are feasible given only the real data?
I have as data some real measures, let's say: 1000, 800, 900, 1100, 900 and I have the Mean Absolute Error (MAE) and Mean Squared Error (MSE) 80 and 20000, but I don't know which are the estimated ...
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mape and mae from k-fold backtesting on time series
I performed a rolling window (i.e. do full sample, then next 4 observations until the last, and so on...) k-fold test for out of sample testing due to limited number of observations.
From the MAPE ...
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1answer
286 views
Is it possible to compute RMSE iteratively?
I am working on continuous evaluation of a regression model on streaming data from sensors. I think that Mean Absolute Error (MAE) can be found out iteratively similar to this link for averaging.
$$
...
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1answer
347 views
Why do I get identical MSE, MAE, DAC from different GARCH specification?
I estimated 5 different garch models using rugarch package as:
...
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240 views
Regression models with comparable MAE but differing R²
I have trained two regression models on the same dataset.
They perform with comparable mean absolut errors $MAE_{1,2} \approx 0.45$, but the coefficient of determination differs significantly with $R^...
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1answer
2k views
Why not using the R squared to measure forecast accuracy?
Why in literature usually the common accuracy measures like MAD, MSE, RMSE, MAPE ... are used. Why not using the $R^2$ (coefficient of determination)?
I was thinking about the difference: By using ...
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1answer
99 views
Forecasting Prediction Accuracy
Out of 4 error paramters which one is best for evaluating prediction accuracy?
Average error
Mean absolute error
Mean squared error
Mean absolute % error
why?
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1answer
410 views
Mean Absolute Error and Data Distribution
I use a memory-based learning model to predict human scores in a [0, 10] range (quiz results). As a forecast error metric I use Mean Absolute Error. I was wondering what is the relation between MAE ...
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1answer
930 views
Measuring forecast accuracy
We're forecasting sales data for one of our clients on a weekly basis. Sales is forecasted for each organizational unit.
The sales data is forecasted via different algorithms and/or algorithm ...
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1answer
11k views
Which is the best accuracy measuring criteria among rmse, mae & mape?
I have created training set and test set from my data. Then I performed auto.arima() and ets() in R on the training set to predict one-step ahead forecasts. These were then compared with the test set ...
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3answers
48k views
Mean absolute error OR root mean squared error?
Why use Root Mean Squared Error (RMSE) instead of Mean Absolute Error (MAE)??
Hi
I've been investigating the error generated in a calculation - I initially calculated the error as a Root Mean ...
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Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)?
MAD = Mean Absolute Deviation
MSE = Mean Squared Error
I've seen suggestions from various places that MSE is used despite some undesirable qualities (e.g. http://www.stat.nus.edu.sg/~staxyc/T12.pdf, ...
