diff --git a/docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc b/docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc
index 01a51894ef5..69d1d7bb629 100644
--- a/docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc
+++ b/docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc
@@ -152,7 +152,10 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed]
 
 `analysis`.`classification`.`num_top_feature_importance_values`::::
 (Optional, integer)
-include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-feature-importance-values]
+Advanced configuration option. Specifies the maximum number of
+{ml-docs}/dfa-classification.html#dfa-classification-feature-importance[feature
+importance] values per document to return. By default, it is zero and no feature importance
+calculation occurs.
 
 `analysis`.`classification`.`training_percent`::::
 (Optional, integer)
@@ -235,7 +238,10 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name]
 
 `analysis`.`regression`.`num_top_feature_importance_values`::::
 (Optional, integer)
-include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-feature-importance-values]
+Advanced configuration option. Specifies the maximum number of
+{ml-docs}/dfa-regression.html#dfa-regression-feature-importance[feature importance] 
+values per document to return. By default, it is zero and no feature importance calculation
+occurs.
 
 `analysis`.`regression`.`training_percent`::::
 (Optional, integer)
diff --git a/docs/reference/ml/ml-shared.asciidoc b/docs/reference/ml/ml-shared.asciidoc
index 0ec04e8087e..e444331a3c5 100644
--- a/docs/reference/ml/ml-shared.asciidoc
+++ b/docs/reference/ml/ml-shared.asciidoc
@@ -904,13 +904,6 @@ total number of categories (in the {version} version of the {stack}, it's two)
 to predict then we will report all category probabilities. Defaults to 2.
 end::num-top-classes[]
 
-tag::num-top-feature-importance-values[]
-Advanced configuration option. If set, feature importance for the top
-most important features will be computed. Importance is calculated
-using the SHAP (SHapley Additive exPlanations) method as described in
-https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf[Lundberg, S. M., & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. In NeurIPS 2017.].
-end::num-top-feature-importance-values[]
-
 tag::over-field-name[]
 The field used to split the data. In particular, this property is used for 
 analyzing the splits with respect to the history of all splits. It is used for