Answers.Body: <p>The <em>terms</em> you choose for the x-axis and y-axis should ideally align with the <em>concepts</em> of most people's intuitive <em>understanding</em> who will view these results. &quot;Confidence Score&quot; of your probability classifier as the predicted maximum class probability itself is a well-understood term in ML and statistics, and accurately describes what your x-axis captures, so no change is needed here. &quot;Assistance Needed&quot; could be the best term comparatively from your candidate list since it essentially measures the spread of all the predicted class probabilities like entropy. Therefore in ML a good term for your y-axis could be <a href="https://arxiv.org/pdf/1502.05312" rel="nofollow noreferrer">&quot;Predictive Entropy&quot;</a> or <a href="https://en.wikipedia.org/wiki/Calibration_(statistics)" rel="nofollow noreferrer">&quot;Confidence Calibration&quot;</a>.</p>
  <blockquote>
  <p>Calibration can mean procedures in statistical classification to determine class membership probabilities which assess the <strong>uncertainty</strong> of a given new observation belonging to each of the already established classes.</p>
  </blockquote>
  
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Answers.Text: But they are two words. Not a single word.
Answers.UserDisplayName: Muhammad Ikhwan Perwira
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Answers.Text: I don't see in OP it specifies to be a single word only. In the meanwhile in applied areas like AI single word terminology might be hard to get a patent since most fundamental fields may have already used them up. So 2-word term is common in AI/ML to make them pick out more definite and distinct descriptions. If you have to coin a single word for your y-axis, I'd suggest just 'entropy' to account for your softmax spread info. Hope this completely clarifies your lingering concern for this question.
Answers.UserDisplayName: cinch
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Answers.Text: Okay I will use *entropy*.
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Body: <p>As we know, classification problems are just a bunch of probabilities, commonly it comes from logits or softmax output. Performing <span class="math-container">$argmax$</span> to get the most favorable class by model, discarding us from some information. Such as the distribution of the probability distribution.</p>
  <p>For deep lerning model that doesn't have reasoning capabilities, So I formulate new term based on model <em>favorable</em> and model <em>asking for validation</em> so I have four quadrants. It's based on purely softmax output only.</p>
  <p>For example, here 8 softmax output with 3 classes:</p>
  <pre><code>Out0 [0.99  0.005 0.005]: Confidence: 0.98, Uncertainty: 0.00
  Out1 [0.8 0.1 0.1]: Confidence: 0.70, Uncertainty: -0.00
  Out2 [0.5  0.25 0.25]: Confidence: 0.25, Uncertainty: 0.00
  Out3 [0.33 0.33 0.33]: Confidence: -0.00, Uncertainty: 0.01
  Out4 [0.99  0.009 0.001]: Confidence: 0.98, Uncertainty: 0.80
  Out5 [0.8  0.19 0.01]: Confidence: 0.70, Uncertainty: 0.90
  Out6 [0.5  0.49 0.01]: Confidence: 0.25, Uncertainty: 0.96
  Out7 [0.1  0.45 0.45]: Confidence: 0.18, Uncertainty: 0.64
  </code></pre>
  <p>Rescaling that range from <span class="math-container">$[0,1]$</span> to <span class="math-container">$[-1, 1]$</span> for visualization, draws:
  <a href="https://i.sstatic.net/7AoTJN5e.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/7AoTJN5e.jpg" alt="enter image description here" /></a></p>
  <p>Then I tried with real data of MNIST handwritten digit of 10 classes. Focusing on label number 1, subjevtively model should be easy for him to classify it, but what if I ask him for something unusual that never seen before, it's 90° degree rotate version of number one.</p>
  <p><a href="https://i.sstatic.net/WixUHJRw.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/WixUHJRw.jpg" alt="enter image description here" /></a></p>
  <p>As I expected, model should be wrong to classify something unusual which is rotated version of number one, and therefore leading to low confidence and high needed assistance.</p>
  <p>If I visualize with the four quadrants, it's clear that the blue (which is rotated sample) is seeking for the validation or assistance compared to red (the original).</p>
  <p>Therefore this is useful for critical inference such as medical diagnoses that doesn't need ground truth for explainable AI.</p>
  <p><a href="https://i.sstatic.net/TMlEeFwJ.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/TMlEeFwJ.jpg" alt="enter image description here" /></a></p>
  <p><strong>The question is, what is the better description to represents x-axis and y-axis?</strong></p>
  <p>As you see I have inconsistent to use the terms for y-axis:</p>
  <ul>
  <li>Uncertainty (I think it doesn't fit with my definition)</li>
  <li>Affirmation (Maybe there is another better)</li>
  <li>Assertion</li>
  <li>Assistance</li>
  <li>Vagueness</li>
  </ul>
  <p>So, what is the best name for this? (no, this is not opinion question, it's objective question that intersect with philosophy)</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-12T10:24:48.983Z
Comments.Text: "Such as the distribution of the probability distribution", ah? The distribution of the probability distribution? What do you mean?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-12T10:28:18.027Z
Comments.Text: Can you explain how each row like `Out0 [0.99  0.005 0.005]: Confidence: 0.98, Uncertainty: 0.00` is computed? How is the confidence and uncertainty computed here?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-12T10:28:47.227Z
Comments.Text: "this is useful for critical inference such as medical diagnoses that doesn't need ground truth for explainable AI." - You don't need ground-truth for medical diagnosis? Really?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-12T10:47:47.400Z
Comments.Text: I said, model doesn't need ground-truth to know whether he is asking for validation of his inference itself. So, when confidence high, but the uncertainty is also high, it's indicator for model which mean he is asking validation by the domain specific expert in this case the doctor specialist.
Comments.UserDisplayName: Muhammad Ikhwan Perwira
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-12T10:50:24.037Z
Comments.Text: The distribution of the probability distribution is local distribution and relative from its the maximum value. With excepting its maximum value, we have distribution of another class (which the model says it's not supposed to be the answer).
Comments.UserDisplayName: Muhammad Ikhwan Perwira
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-12T10:58:05.497Z
Comments.Text: How I compute `Confidence` is simply taking the maximum value of **a class** and then normalized it relatively with its mean.
  
  While `Uncertainty` simply comparing the most minimum with relative uniform value (excepting value that has the most maximum). Therefore, uncertainty is representing entropy and how assert is the model sure he doesn't know its inference itself based on **another classes** consideration.
Comments.UserDisplayName: Muhammad Ikhwan Perwira
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-12T11:25:20.237Z
Comments.Text: The quadrant of *Don't Trust My Inference* is related with Socrates's philosophy. [*I know that I know nothing*](https://en.wikipedia.org/wiki/I_know_that_I_know_nothing), therefore *Don't Trust My Inference!*.
Comments.UserDisplayName: Muhammad Ikhwan Perwira
Tags: <classification><philosophy><explainable-ai><softmax>
Title: What is it called when model asking for validation?
UserDisplayName: Muhammad Ikhwan Perwira
ThreadText: # What is it called when model asking for validation?
  
  (Asked by: username_0 on 2024-12-12)
  
  As we know, classification problems are just a bunch of probabilities, commonly it comes from logits or softmax output. Performing $argmax$ to get the most favorable class by model, discarding us from some information. Such as the distribution of the probability distribution.
  
  For deep lerning model that doesn't have reasoning capabilities, So I formulate new term based on model *favorable* and model *asking for validation* so I have four quadrants. It's based on purely softmax output only.
  
  For example, here 8 softmax output with 3 classes:
  
  ```
  Out0 [0.99  0.005 0.005]: Confidence: 0.98, Uncertainty: 0.00
  Out1 [0.8 0.1 0.1]: Confidence: 0.70, Uncertainty: -0.00
  Out2 [0.5  0.25 0.25]: Confidence: 0.25, Uncertainty: 0.00
  Out3 [0.33 0.33 0.33]: Confidence: -0.00, Uncertainty: 0.01
  Out4 [0.99  0.009 0.001]: Confidence: 0.98, Uncertainty: 0.80
  Out5 [0.8  0.19 0.01]: Confidence: 0.70, Uncertainty: 0.90
  Out6 [0.5  0.49 0.01]: Confidence: 0.25, Uncertainty: 0.96
  Out7 [0.1  0.45 0.45]: Confidence: 0.18, Uncertainty: 0.64
  
  ```
  
  Rescaling that range from $[0,1]$ to $[-1, 1]$ for visualization, draws:
  [![enter image description here](https://i.sstatic.net/7AoTJN5e.jpg)](https://i.sstatic.net/7AoTJN5e.jpg)
  
  Then I tried with real data of MNIST handwritten digit of 10 classes. Focusing on label number 1, subjevtively model should be easy for him to classify it, but what if I ask him for something unusual that never seen before, it's 90° degree rotate version of number one.
  
  [![enter image description here](https://i.sstatic.net/WixUHJRw.jpg)](https://i.sstatic.net/WixUHJRw.jpg)
  
  As I expected, model should be wrong to classify something unusual which is rotated version of number one, and therefore leading to low confidence and high needed assistance.
  
  If I visualize with the four quadrants, it's clear that the blue (which is rotated sample) is seeking for the validation or assistance compared to red (the original).
  
  Therefore this is useful for critical inference such as medical diagnoses that doesn't need ground truth for explainable AI.
  
  [![enter image description here](https://i.sstatic.net/TMlEeFwJ.jpg)](https://i.sstatic.net/TMlEeFwJ.jpg)
  
  **The question is, what is the better description to represents x-axis and y-axis?**
  
  As you see I have inconsistent to use the terms for y-axis:
  
  * Uncertainty (I think it doesn't fit with my definition)
  * Affirmation (Maybe there is another better)
  * Assertion
  * Assistance
  * Vagueness
  
  So, what is the best name for this? (no, this is not opinion question, it's objective question that intersect with philosophy)
  
  **Tags:** classification, philosophy, explainable-ai, softmax
  
  > **Comments:**
  > **username_1**: "Such as the distribution of the probability distribution", ah? The distribution of the probability distribution? What do you mean?  
  > **username_1**: Can you explain how each row like `Out0 [0.99  0.005 0.005]: Confidence: 0.98, Uncertainty: 0.00` is computed? How is the confidence and uncertainty computed here?  
  > **username_1**: "this is useful for critical inference such as medical diagnoses that doesn't need ground truth for explainable AI." - You don't need ground-truth for medical diagnosis? Really?  
  > **username_0**: I said, model doesn't need ground-truth to know whether he is asking for validation of his inference itself. So, when confidence high, but the uncertainty is also high, it's indicator for model which mean he is asking validation by the domain specific expert in this case the doctor specialist.  
  > **username_0**: The distribution of the probability distribution is local distribution and relative from its the maximum value. With excepting its maximum value, we have distribution of another class (which the model says it's not supposed to be the answer).  
  > **username_0**: How I compute `Confidence` is simply taking the maximum value of **a class** and then normalized it relatively with its mean.  While `Uncertainty` simply comparing the most minimum with relative uniform value (excepting value that has the most maximum). Therefore, uncertainty is representing entropy and how assert is the model sure he doesn't know its inference itself based on **another classes** consideration.  
  > **username_0**: The quadrant of *Don't Trust My Inference* is related with Socrates's philosophy. [*I know that I know nothing*](https://en.wikipedia.org/wiki/I_know_that_I_know_nothing), therefore *Don't Trust My Inference!*.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2024-12-12. Score: 2 | Accepted answer
  
  The *terms* you choose for the x-axis and y-axis should ideally align with the *concepts* of most people's intuitive *understanding* who will view these results. "Confidence Score" of your probability classifier as the predicted maximum class probability itself is a well-understood term in ML and statistics, and accurately describes what your x-axis captures, so no change is needed here. "Assistance Needed" could be the best term comparatively from your candidate list since it essentially measures the spread of all the predicted class probabilities like entropy. Therefore in ML a good term for your y-axis could be ["Predictive Entropy"](https://arxiv.org/pdf/1502.05312) or ["Confidence Calibration"](https://en.wikipedia.org/wiki/Calibration_(statistics)).
  
  > Calibration can mean procedures in statistical classification to determine class membership probabilities which assess the **uncertainty** of a given new observation belonging to each of the already established classes.
  
  > **Comments:**
  > **username_0**: But they are two words. Not a single word.  
  > **username_2**: I don't see in OP it specifies to be a single word only. In the meanwhile in applied areas like AI single word terminology might be hard to get a patent since most fundamental fields may have already used them up. So 2-word term is common in AI/ML to make them pick out more definite and distinct descriptions. If you have to coin a single word for your y-axis, I'd suggest just 'entropy' to account for your softmax spread info. Hope this completely clarifies your lingering concern for this question.  
  > **username_0**: Okay I will use *entropy*.  
  
  ---
  
  