Refreshes screenshot; removes comments

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

@@ -44,15 +44,6 @@ 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.
 
-////
-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
 `num_top_feature_importance_values` property. For example, see