Answers.Body: <p>Mean Absolute Error is nothing but the mean of absolute errors.</p>
  <p>If your model gave <span class="math-container">$n$</span> predictions <span class="math-container">$\{\hat{y}_i\}_{i = 1}^{n}$</span> against <span class="math-container">$n$</span> ground truths <span class="math-container">$\{y_i\}_{i = 1}^{n}$</span>, then MAE is defines as follows</p>
  <p><span class="math-container">$$MAE_{model} = \dfrac{\sum\limits_{i = 1}^{n} |y_i - \hat{y}_i|}{n} $$</span>.</p>
  <p>Thus, MAE gives the average amount of error. So, the machine learning model with minimum MAE should be considered as a good model since it is giving minimum error.</p>
  <p>In your task, SVM should be considered as a good model.</p>
  <p>The range of <span class="math-container">$MAE$</span> is <span class="math-container">$[0,\infty)$</span>. The lower bound zero is achieved if all your model's predictions are exactly correct, else it will give positive value depending on how worse your model's predictions are.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-07-29T07:26:36.587Z
Answers.Text: Additional to @hanugm's response, whether a MAE is good or not depends on data. If you're predicting millions and MAE is 100, sounds good. If you're predicting 2 digits and MAE is 100, it's not good. For this reason, there's another metric called MAPE: Mean absolute percentage error.
Answers.UserDisplayName: meliksahturker
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-07-29T06:21:53.883Z
Answers.LastActivityDate: 2021-07-29T06:21:53.883Z
Answers.UserDisplayName: hanugm
Body: <p>I have just run an MAE calculation for my machine learning models and the results show:</p>
  <blockquote>
  <p>SVM MAE = 28.850 deg.</p>
  <p>Random Forest MAE = 33.832 deg.</p>
  </blockquote>
  <p>How do I know what a good MAE value is? What is the range of the MAE?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-31T13:13:00.267Z
Comments.Text: Hello. This question was closed as "needs more details". Not sure why, but people probably wanted you to describe what you didn't understand about MAE when you read about it on the web. In other words, if you haven't done a little bit of research before asking this question, please, next time, do a little bit of research before asking a question. Some questions are so basic that answers can be immediately found as the first entry of a google search. In any case, you already got an answer to your question, so there's no strict need to reopen this post.
Comments.UserDisplayName: nbro
Tags: <machine-learning><performance><metric>
Title: How do I know what a good mean absolute error value is?
UserDisplayName: user48902
ThreadText: # How do I know what a good mean absolute error value is?
  
  (Asked by: username_0 on 2021-07-29)
  
  I have just run an MAE calculation for my machine learning models and the results show:
  
  > SVM MAE = 28.850 deg.
  >
  > Random Forest MAE = 33.832 deg.
  
  How do I know what a good MAE value is? What is the range of the MAE?
  
  **Tags:** machine-learning, performance, metric
  > **Comments:**
  > **username_1**: Hello. This question was closed as "needs more details". Not sure why, but people probably wanted you to describe what you didn't understand about MAE when you read about it on the web. In other words, if you haven't done a little bit of research before asking this question, please, next time, do a little bit of research before asking a question. Some questions are so basic that answers can be immediately found as the first entry of a google search. In any case, you already got an answer to your question, so there's no strict need to reopen this post.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2021-07-29. Score: 1 | Accepted answer
  
  Mean Absolute Error is nothing but the mean of absolute errors.
  
  If your model gave $n$ predictions $\{\hat{y}\_i\}\_{i = 1}^{n}$ against $n$ ground truths $\{y\_i\}\_{i = 1}^{n}$, then MAE is defines as follows
  
  $$MAE\_{model} = \dfrac{\sum\limits\_{i = 1}^{n} |y\_i - \hat{y}\_i|}{n} $$.
  
  Thus, MAE gives the average amount of error. So, the machine learning model with minimum MAE should be considered as a good model since it is giving minimum error.
  
  In your task, SVM should be considered as a good model.
  
  The range of $MAE$ is $[0,\infty)$. The lower bound zero is achieved if all your model's predictions are exactly correct, else it will give positive value depending on how worse your model's predictions are.
  
  > **Comments:**
  > **username_3**: Additional to @hanugm's response, whether a MAE is good or not depends on data. If you're predicting millions and MAE is 100, sounds good. If you're predicting 2 digits and MAE is 100, it's not good. For this reason, there's another metric called MAPE: Mean absolute percentage error.  
  
  ---
  
  