diff --git a/docs/en/stack/ml/df-analytics/ml-feature-importance.asciidoc b/docs/en/stack/ml/df-analytics/ml-feature-importance.asciidoc
index 015530b493..ac0a1850e6 100644
--- a/docs/en/stack/ml/df-analytics/ml-feature-importance.asciidoc
+++ b/docs/en/stack/ml/df-analytics/ml-feature-importance.asciidoc
@@ -44,7 +44,12 @@ data point to that baseline, you arrive at the numeric prediction value. If a
 {feat-imp} value is negative, it reduces the prediction value. If a {feat-imp}
 value is positive, it increases the prediction value.
 
-//TBD: Add section about classification analysis.
+For {classanalysis}, the baseline is the average of the probability values for a
+specific class across all the data points in the training data set. When you add
+the feature importance values for a particular data point to that baseline, you
+arrive at the prediction probability for that class. If a {feat-imp} value is
+negative, it reduces the prediction probability. If a {feat-imp} value is
+positive, it increases the prediction probability.
 
 By default, {feat-imp} values are not calculated. To generate this information,
 when you create a {dfanalytics-job} you must specify the