docs causal fidelity: add remarks

docs/api/deletion.md

@@ -10,7 +10,29 @@ the important pixels.
 
     -- <cite>[RISE: Randomized Input Sampling for Explanation of Black-box Models (2018)](https://arxiv.org/abs/1806.07421)</cite>[^1]
 
-The better the method, the smaller the score.
+
+## Score interpretation
+
+If explanations are accurate, the score will quickly fall from the score on non-perturbed input to the score of a random predictor.
+  Thus, in this case, a lower score represent a more accurate explanation.
+
+
+## Remarks
+
+This metric only evaluate the order of importance between features.
+
+The parameters metric, steps and max_percentage_perturbed may drastically change the score :
+
+- For inputs with many features, increasing the number of steps will allow you to capture more efficiently the difference between attributions methods.
+
+- The order of importance of features with low importance may not matter, hence, decreasing the max_percentage_perturbed,
+may make the score more relevant.
+
+Sometimes, attributions methods also returns negative attributions,
+for those methods, do not take the absolute value before computing insertion and deletion metrics.
+Otherwise, negative attributions may have higher absolute values, and the order of importance between features will change.
+Therefore, the score may not represent the pertinence of the explanation.
+
 
 ## Example
 

docs/api/insertion.md

@@ -10,7 +10,29 @@ The Insertion Fidelity metric measures how well a saliency-map–based explanati
 
     -- <cite>[RISE: Randomized Input Sampling for Explanation of Black-box Models (2018)](https://arxiv.org/abs/1806.07421)</cite>[^1]
 
-The better the method, the higher the score.
+
+## Score interpretation
+
+If explanations are accurate, the score will quickly rise to the score on non-perturbed input.
+  Thus, in this case, a higher score represent a more accurate explanation.
+
+
+## Remarks
+
+This metric only evaluate the order of importance between features.
+
+The parameters metric, steps and max_percentage_perturbed may drastically change the score :
+
+- For inputs with many features, increasing the number of steps will allow you to capture more efficiently the difference between attributions methods.
+
+- The order of importance of features with low importance may not matter, hence, decreasing the max_percentage_perturbed,
+may make the score more relevant.
+
+Sometimes, attributions methods also returns negative attributions,
+for those methods, do not take the absolute value before computing insertion and deletion metrics.
+Otherwise, negative attributions may have higher absolute values, and the order of importance between features will change.
+Therefore, the score may not represent the pertinence of the explanation.
+
 
 ## Example