Answers.Body: <p>I support what Dr. Snoopy said. Other metrics include AUC (area under the curve), precision, recall, sensitivity, specificity, positive predicted value, negative predicted value,...</p>
  <p>It ultimately depends on the context that you are deploying your model, and what the best standards to judge by are in the field.</p>
  
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Answers.CreationDate: 2022-07-03T19:03:53.297Z
Answers.LastActivityDate: 2022-07-03T19:03:53.297Z
Answers.UserDisplayName: sma
Answers.Body: <p>No, accuracy is not by far the best or the only measure you should look. It is not universal, accuracy only applies for classification problems, if you do regression then accuracy cannot be used.</p>
  <p>There are a lot of metrics that can be used to monitor learning and predictive performance of models, there are too many to mention here. But the very important point, is that you should select metrics according to the problem and the desired behavior for the model, there is no best for this exact reason, since every problem is different.</p>
  <p>It is very important to always think on the loss and metrics to be used for your model and problem, and not to take default choices.</p>
  
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Answers.CreationDate: 2022-07-04T02:45:17.850Z
Answers.Text: Can you please give some use cases when to use loss as metrics over accuracy?
Answers.UserDisplayName: Dilip C M Dept of MCA
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Answers.UserDisplayName: Dr. Snoopy
Body: <p>Consider a model <code>A</code> that achieved an test accuracy of 99% on dataset-A with the size of 200 images and a model <code>B</code> that achieved only 50% on dataset-B with a size of 50,000 images. Also consider both the datasets split into train,validation and test sets in the ratio of 0.8,0.1,0.1.</p>
  <p>But on test data of dataset-B,the model A is failed to attain same accuracy in fact it is giving lesser accuracy that of model B.</p>
  <p>So, is the accuracy always the best measure to evaluate the performance of the DL model? Or any other better performance metrics available?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-03T20:30:38.953Z
Comments.Text: What do you mean by "But in real real-time, model B is more robust than model A."? Do you mean that when you use B to predict on the real data B has better accuracy than A? What do you mean by "more robust" here?
Comments.UserDisplayName: nbro
Tags: <deep-learning><performance><accuracy>
Title: Is the accuracy the best metrics to evaluate the performance of Deep Learning model?
UserDisplayName: Dilip C M Dept of MCA
ThreadText: # Is the accuracy the best metrics to evaluate the performance of Deep Learning model?
  
  (Asked by: username_0 on 2022-07-03)
  
  Consider a model `A` that achieved an test accuracy of 99% on dataset-A with the size of 200 images and a model `B` that achieved only 50% on dataset-B with a size of 50,000 images. Also consider both the datasets split into train,validation and test sets in the ratio of 0.8,0.1,0.1.
  
  But on test data of dataset-B,the model A is failed to attain same accuracy in fact it is giving lesser accuracy that of model B.
  
  So, is the accuracy always the best measure to evaluate the performance of the DL model? Or any other better performance metrics available?
  
  **Tags:** deep-learning, performance, accuracy
  
  > **Comments:**
  > **username_1**: What do you mean by "But in real real-time, model B is more robust than model A."? Do you mean that when you use B to predict on the real data B has better accuracy than A? What do you mean by "more robust" here?  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2022-07-03. Score: 1
  
  I support what username_3 said. Other metrics include AUC (area under the curve), precision, recall, sensitivity, specificity, positive predicted value, negative predicted value,...
  
  It ultimately depends on the context that you are deploying your model, and what the best standards to judge by are in the field.
  
  
  ---
  
  ### Answer by username_3 on 2022-07-03. Score: 1
  
  No, accuracy is not by far the best or the only measure you should look. It is not universal, accuracy only applies for classification problems, if you do regression then accuracy cannot be used.
  
  There are a lot of metrics that can be used to monitor learning and predictive performance of models, there are too many to mention here. But the very important point, is that you should select metrics according to the problem and the desired behavior for the model, there is no best for this exact reason, since every problem is different.
  
  It is very important to always think on the loss and metrics to be used for your model and problem, and not to take default choices.
  
  > **Comments:**
  > **username_0**: Can you please give some use cases when to use loss as metrics over accuracy?  
  
  ---
  
  