Answers.Body: <p>After several tests, I've opted for the following configuration:</p>
  <p>Approximation mode: riemann trapezoidal
  Steps: 1000
  Baseline: average mean of each column (feature)</p>
  <p>Although the global score remains good, there're important variations in the number of correctly explained local samples. For example, an execution returns 200 out of 390 samples explained as expected, but the following execution only 17.</p>
  <p>Is there a reason for that? And, is there a way to make the algorithm predictable?</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-05-13T13:54:50.913Z
Answers.LastActivityDate: 2021-05-13T13:54:50.913Z
Answers.UserDisplayName: heisenberg
Body: <p>I have a linear tabular dataset made of floats. The dataset follows a simple rule like:</p>
  <pre><code>if features A and B are in a certain range then target class is 1, otherwise target class is 0.
  </code></pre>
  <p>Since I want to get some interpretability from my ANN model, I opted for using the integrated gradients method implemented by alibi.</p>
  <p>Unfortunately, most of individual samples don't show A and B as the leading features as expected. Even more weird is the fact that, when I average the attributions of all the individual samples, A and B get the highest score. In other words, local explanations fail but, on average, the global explanation is correct.</p>
  <p>Can anyone help me out to understand why this happens? Isn't integrated gradients method suitable for tabular datasets?</p>
  <p>By the way, my baseline is based on a uniform distribution of random floats ranging from 0 to the maximum of each column.</p>
  
Tags: <deep-learning><math><explainable-ai><gradient>
Title: Why don't integrated gradients explain samples correctly?
UserDisplayName: user46784
ThreadText: # Why don't integrated gradients explain samples correctly?
  
  (Asked by: username_0 on 2021-05-05)
  
  I have a linear tabular dataset made of floats. The dataset follows a simple rule like:
  
  ```
  if features A and B are in a certain range then target class is 1, otherwise target class is 0.
  
  ```
  
  Since I want to get some interpretability from my ANN model, I opted for using the integrated gradients method implemented by alibi.
  
  Unfortunately, most of individual samples don't show A and B as the leading features as expected. Even more weird is the fact that, when I average the attributions of all the individual samples, A and B get the highest score. In other words, local explanations fail but, on average, the global explanation is correct.
  
  Can anyone help me out to understand why this happens? Isn't integrated gradients method suitable for tabular datasets?
  
  By the way, my baseline is based on a uniform distribution of random floats ranging from 0 to the maximum of each column.
  
  **Tags:** deep-learning, math, explainable-ai, gradient
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-05-13. Score: 0
  
  After several tests, I've opted for the following configuration:
  
  Approximation mode: riemann trapezoidal
  Steps: 1000
  Baseline: average mean of each column (feature)
  
  Although the global score remains good, there're important variations in the number of correctly explained local samples. For example, an execution returns 200 out of 390 samples explained as expected, but the following execution only 17.
  
  Is there a reason for that? And, is there a way to make the algorithm predictable?
  
  
  ---
  
  