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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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in ann model statistical calculation can mae be greater than rmse? [duplicate]

in ann modelling creation can mae be greater than rmse? i am using the LMNN algorithm for creting the ann model.i am having the values as said above in the performance evaluation of the ann model.can ...
Tapaswini Mohanty's user avatar
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Standardizing between time sensitive datasets

I have two datasets for property values in 2020 and 2024, in which I've created two separate models to predict property value. I want to compare the output of these models by using the mean absolute ...
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Machine learning benchmarks: MAE, RMSE, and R-squared

I'm working on a machine learning problem, and I'm having trouble interpreting different measures of model performance. I have a single dependent variable (proportion change between two treatments, ...
S. Robinson's user avatar
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Can we use decomposed time series in a test set to obtain models accuracy metrics?

Currently I'm cross validating (rolling time window, different test lengths) several forecast models to obtain performance comparison on highly skewed time series (due to true ocassional outliers). My ...
Tom's user avatar
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How boxplot over absolute error of learning models could be used to compare\evaluate learning models' performances?

Recently I crossed this paper which represents the evaluation of various models' performances within a single dataset by Boxplot over $Absolute~Error~(AE)$ as follows: Fig. 12: Boxplot of baseline ...
Mario's user avatar
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What is an intuitive explanation for why my mean absolute error converges to 66.6%?

Let's assume I have actual numbers (randomly generated) between 1 and 1000. Let's further assume, my prediction model tries to predict the actual numbers. My "forecasting" model is a monkey ...
UDE_Student's user avatar
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20 views

Interpret Mean vs. SMAPE results [duplicate]

I have following dataframe: ...
Maxl Gemeinderat's user avatar
4 votes
2 answers
2k views

Can someone help me understand why the MAE, MSE and RMSE scores for my regression model are very low but the R2 is negative?

I am using a random forest regression model to make predictions and leave one out cross validation for my prediction task. I am having a difficult time understanding why my R2 score is negative when ...
Rai's user avatar
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Why can LASSO MAE be worse than individual feature linear regression MAE?

I am comparing the MAE of LASSO regression of multiple features vs. MAE of linear regression of each individual feature, and I am having trouble understanding why the LASSO MAE can be worse than some ...
Anna's user avatar
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1 answer
338 views

Equivalent of $E[(a-X)^2] = E[(a-E(X))^2] + Var(X)$ for $E[|a-X|]$ and $med(X)$?

The minimzer of the MSE $E[(a-X)^2]$ is $a=E(X)$, and the MSE can be decomposed into $E[(a-X)^2] = E[(a-E(X))^2] + Var(X)$. I am wondering whether there exists a similar expression th MAE $E[|a-X|]$ ...
FZS's user avatar
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A Higher r-squared always implies a reduction in MAE and RMSE?

I apply 2 different machine learning models in my data, a Multiple Linear Regression and Random Forest. The results were bellow: Why the MAE and RMSE are higher for a higher R-squared? Both models ...
Alice Silva's user avatar
1 vote
1 answer
353 views

Percentage change in RMSE (or MAE) over models

Let's say I have two different models of an outcome Y, m1 and m2 and perform some kind of cross-validation. I calculate the RMSE and the MAE on the test set (for the two models) and I want to say ...
MTSOC's user avatar
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1 vote
2 answers
450 views

Giving more importance to under prediction (mean absolute error) than over prediction for forecasting

Just curious to hear any thoughts on weighting over prediction in mean absolute error to minimize the penalty since I'm more interested in under prediction, if that makes sense. Basically, I'm ...
theduker's user avatar
1 vote
1 answer
2k views

How to interpret MSE, RMSE and MAE

I understand in general MSE, RMSE and MAE means average distance between the actual and predicted value, and the lower the MSE, RMSE and MAE, the better the model fits the dataset. I try to understand ...
user032020's user avatar
5 votes
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4k views

MAE vs MSE for Linear regression

Several articles says that MAE is robust to outliers but MSE is not and MSE can hamper the model if errors are too huge. My question is that MSE and MAE both are error matrices, our priority is to ...
Parth Sharma's user avatar
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1 answer
149 views

Are MAE and RMSE possible ways of choosing between ols and types of glm?

I have a longitudinal data set and have fit a number of different regression models [ols, poisson, binomial....and more]. I want to justify the final model selection and assumed one method might be to ...
j.rahilly's user avatar
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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
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Why can't I reproduce the MASE of a time series manually as in done in the Fable package?

I am trying to produce the Mean Absolute Scaled Error (MASE) for a custom time series model to compare its performance in forecasting several indicators with different units. I wanted to check my ...
Alex's user avatar
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1 answer
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How to approximate MAE of monthly values from MAE of daily values?

Suppose I have the Mean Absolute Error (MAE) of daily values for a period of, say, 1 year. Assume the errors are normally distributed. The value for a month is equal to the sum of the values for each ...
Charles Del Lar's user avatar
1 vote
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58 views

MAPE does not take into account the range of the output?

I have a time-series regression model where the output is always in the range of 6000-6050. After training my model, I get a Mean Absolute Error of around 18 and hence, very low Mean Absolute ...
dayyda's user avatar
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is Mean Absolute Error (MAE) unique?

Reading this answer as to why minimizing MAE results in median forecasts, I did not fully understand why MAE is not unique! What is exactly meant by this? Can someone give a specific example of ...
jj_coder's user avatar
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242 views

Showing RMSE and MAE results as percentage error

I have the results of RMSE and MAE from different spatial interpolation methods as a monthly averages (See the figure below). As ...
Mukhtar's user avatar
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3 votes
2 answers
2k views

Alternative to mean absolute percentage error (MAPE)

MAPE metric has problems when the actual value to be predicted is very small. In the extreme when the actual value is 0 then MAPE will be infinity (if the prediction is not exactly 0). What about this ...
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Is there a single regression quality metric for the median and the 95% percentile?

I want to evaluate the quality of prediction of two values the median and 95% percentile of a distribution. Is there a standard way to do this? I have thought about using "Mean Mean Average Error&...
andandandand's user avatar
1 vote
1 answer
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Why can the aggregated prediction of two bad models be better than the one two very good models?

I am modeling two separated processes (e.g., number of events A and B). As jointly modeling both processes is not possible, I estimate for each process three different models. When I look at the three ...
majom's user avatar
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What optimisation method is used in ordered logistic regression?

I am using the polr function in R to create an ordered logistic model and am curious to know what its optimisation method is? It seems to perform better than other models I have tested it against when ...
Markos's user avatar
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1 answer
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Interpretation of mean absolute error in rpart

I ran rpart on my dataset (3000x9) to predict adolescent GPA (continuous), made predictions on test data, and found the mean absolute error to be in the range of 0.45-0.48. Does this mean that the ...
Tannya Kumar's user avatar
1 vote
0 answers
235 views

High RMSE and High MAE in Autoencoder Regression

I have been developing a simple autoencoder model using PyTorch by which I am training the reconstructed output to be the same and input and also do regression on the hidden layer to predict a single ...
Sahil Yerawar's user avatar
7 votes
2 answers
12k views

Why getting very high values for MSE/MAE/MAPE when R2 score is very good

I am applying different regression models (RF, Knn, etc) on some well-known datasets (bike sharing, diabetics, etc). The value of R2 is very good. From the R2 score,...
Opps_0's user avatar
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Model performance in time-series forecasting with some outliers

I'm creating forecasts for products where some of them have large seasonal spike during times like Christmas and/or Easter but relatively low sales volume on other times. For this particular product ...
Viðar Ingason's user avatar
1 vote
1 answer
1k views

Using standardized values (z-score) for MAE (Mean absolute error)

I have two models/indices that try to predict observed values. I've compared them using correlation and regression, but I'd like to use MAE (Mean absolute error) to asses which of them is more closer ...
Jusba's user avatar
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Out-of-sample MSE and MAE for volatility forecasting [duplicate]

I have been searching through the whole CrossValidated but couldn't find the answer. I want to test out-of-sample the volatility forecasts (if it means something ARCH-like ones, MSGARCH, Multifractal ...
Selena Pepic's user avatar
8 votes
1 answer
1k views

Why is there no improvement when training Xgboost with pseudo-Huber loss?

In this StackOverflow post I asked if there was something wrong with my syntax when training an XGboost model (in R) with the native pseudo-Huber loss ...
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Does Data Normalization affect RMSE & MAE?

Suppose I am using two different methods to perform regression, such as ARIMA (1) and a Neural Network (2), and I am using the RMSE and MAE metrics to measure accuracy. For method (1), I do not ...
user9927059's user avatar
1 vote
1 answer
222 views

Choosing metric for regression problem RMSE or MAE

I have a regression task where I want to predict no of product sold by manufacturer. My data is time series with each day having a certain no of units sold. I'm slightly torn as to which metric to use....
mathella's user avatar
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1 answer
1k views

Is this the right way to evaluate MSE, RMSE, MAE, MAPE after creating a model on data passed through a standard scaler?

Create the Scaler object scaler = preprocessing.StandardScaler() Fit your training data on the scaler object ...
Parag Ahire's user avatar
1 vote
2 answers
2k views

What is the interpretation of the negative value for the Normalized Mean Absolute Error (nMAE) metric?

I am using the normalized mean absolute error metric for evaluating my results. The data I use is in time-series form. Their trend may be increasing or decreasing over time. All the values are ...
elldora's user avatar
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4 votes
2 answers
3k views

Can MAE be reported in percentage?

In Wang et al. "Predicting New Workload or CPU Performance by Analyzing Public Datasets" (2019), the MAE is measured in a standardized space (Mean=0, SD=1). This is discussed in 3.4 Metrics, ...
towi_parallelism's user avatar
1 vote
2 answers
5k views

MAPE is better but MAE is worse in regression models

I am working on a regression problem to predict price of the vehicle based on its features. I have been experimenting with several trials but in one of them, MAPE (Mean Absolute Percentage Error) is ...
user3447653's user avatar
2 votes
1 answer
967 views

What is mean average error?

I saw that in some articles but I couldn't find any definition. Its unit is in the following form: MAE/mean(MAE)
Chruz Roman's user avatar
1 vote
0 answers
177 views

huge difference between RMSE and MAE in non-linear regressors

I am building non-linear regression models such as a Random Forest regressor , a KNN regressor or a SVM using a RBF kernel and I decided to use both RMSE and MAE as evaluation metrics. I know that a ...
Kyriaki Poursaitidou's user avatar
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0 answers
612 views

Mean average error normalized by the standard deviation of the target

I'm working on a regression model, aiming at prediction age from structured data. I'm using the mean average error (MAE) as evaluation metric, and want to compare my performances with state-of-the-art ...
Clément POIRET's user avatar
3 votes
1 answer
454 views

MAE regression gives biased regression parameters for symmetric error?

Consider a linear model, $$ y_i = \beta_0 + \beta_1x_{1i} + \beta_2x_{2i} + \epsilon_i. $$ From the Gauss-Markov theorem, I know that, under nice conditions, the $\hat{\beta}_{OLS}=(X^TX)^{-1}X^Ty$ ...
Dave's user avatar
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How should i compare this MAE results

I'm in need of help to correctly interpret these results. I am analyzing a time series of crime data. The series has 351,980 events. These are the results of the predictions using a regression model:...
H. Joner's user avatar
2 votes
1 answer
27k views

How do I interpret mean absolute error (MAE) or mean absolute percentage error (MAPE) in layman words?

For example, I am predicting a score that can have value from 0 to 100. Lets assume MAPE = 10...
vasili111's user avatar
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2 votes
1 answer
417 views

Scaling the MAE by the mean of non zero points for intermittent data

I am currently trying to find a way of scaling the MAE for my intermittent data. The data is always greater than 0 and is intermittent, with long periods of zeros. I have read a few papers that ...
OliverBrace's user avatar
2 votes
1 answer
213 views

What does it mean when MAE improved while RMSE worsened?

I am comparing two models: One is a black box that I cannot understand, the other is a GLM. How can I describe why the differences are like this? Is the GLM performing worse than the blackbox?
Seraphim's user avatar
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4 votes
1 answer
2k views

Quantile loss 50th is MAE, is it? [duplicate]

I'm not sure the above sentence is true, but I read it here, here and here that quantile loss function percentile 0.5 is MAE (mean absolute error). Is it true (yes or no)? And How?
Farshid Shekari's user avatar
2 votes
2 answers
615 views

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 ...
Anne Bierhoff's user avatar
2 votes
2 answers
121 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 ...
Perl Del Rey's user avatar