Revert changes to feature importance concept

docs/en/stack/ml/df-analytics/ml-feature-importance.asciidoc

@@ -44,6 +44,8 @@ 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.
+
 By default, {feat-imp} values are not calculated. To generate this information,
 when you create a {dfanalytics-job} you must specify the
 `num_top_feature_importance_values` property. For example, see
@@ -63,4 +65,4 @@ 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].
 
 See also
-https://www.elastic.co/blog/feature-importance-for-data-frame-analytics-with-elastic-machine-learning[{feat-imp-cap} for {dfanalytics} with Elastic {ml}].
+https://www.elastic.co/blog/feature-importance-for-data-frame-analytics-with-elastic-machine-learning[{feat-imp-cap} for {dfanalytics} with Elastic {ml}].