[DOCS] More edits

docs/en/stack/ml/df-analytics/flightdata-classification.asciidoc

@@ -122,7 +122,7 @@ impacting accuracy.
 .. If you want to experiment with <<ml-feature-importance,feature importance>>,
 specify a value in the advanced configuration options. In this example, we
 choose to return a maximum of 10 feature importance values per document. This
-option affects the speed of the analysis, however, so by default it is disabled. 
+option affects the speed of the analysis, so by default it is disabled. 
 .. Use the default memory limit for the job. If the job requires more than this 
 amount of memory, it fails to start. If the available memory on the node is
 limited, this setting makes it possible to prevent job execution.
@@ -324,14 +324,13 @@ or testing data set. You can filter the table and the confusion matrix such that
 they contain only testing or training data. You can also enable histogram charts
 to get a better understanding of the distribution of values in your data.
 
-If you examine this destination index more closely in the *Discover* app in 
-{kib} or use the standard {es} search command, you can see that the analysis 
-predicts the probability of all possible classes for the dependent variable (in 
-a `top_classes` object). In this case, there are two classes: `true` and 
-`false`. The most probable class is the prediction, which is what's shown in the
-{classification} results table. If you want to understand how sure the model is
-about the prediction, however, you might want to examine the class probability
-values. A higher number means that the model is more confident.
+If you want to understand how certain the model is about each prediction, you
+can examine its probability and score (`ml.prediction_probability` and
+`ml.prediction_score`). These values range between 0 and 1; the higher the
+number, the more confident the model is that the data point belongs to the named
+class. If you examine this destination index more closely in the *Discover* app
+in {kib} or use the standard {es} search command, you can see that the analysis 
+predicts the probability of all possible classes for the dependent variable. The `top_classes` object contains the predicted classes with the highest scores.
 
 .API example
 [%collapsible]
@@ -403,31 +402,42 @@ The snippet below shows a part of a document with the annotated results:
 <1> An array of values specifying the probability of the prediction and the
 score for each class. 
 
-The `top_classes` object contains the predicted classes with the highest scores.
-Each score has a value between 0 and 1. The higher the number, the more
-confident the model is that the data point belongs to the named class. In the
-example above, `false` has a `class_score` of 0.39 while `true` has only 0.22,
-so the prediction will be `false`. The score and probability for the chosen
-class also appear in the `prediction_probability` and `prediction_score` fields. 
-For more details about these values, see <<dfa-classification-interpret>>.
+The class with the highest score is the prediction. In this example, `false` has
+a `class_score` of 0.39 while `true` has only 0.22, so the prediction will be
+`false`. For more details about these values, see
+<<dfa-classification-interpret>>.
 ====
 
 If you chose to calculate feature importance, the destination index also
-contains `feature_importance` objects. This information indicates which fields
-(also known as _features_ of a data point) had the biggest impact on each
-prediction. In {kib}, you can see this information displayed in the form of a
-decision plot:
+contains `ml.feature_importance` objects. Every field that is included in the 
+{classanalysis} (known as a _feature_ of the data point) is assigned a feature
+importance value. However, only the most significant values (in this case, the
+top 10) are stored in the index. These values indicate which features had the
+biggest impact (positive or negative) on each prediction. In {kib}, you can see
+this information displayed in the form of a decision plot:
 
 [role="screenshot"]
 image::images/flights-classification-importance.png["A decision plot for feature importance values in {kib}"]
 
-The values in the decision plot are sorted in descending order, such that the
-features with the most significant positive or negative impact appear at the top.
-Each feature importance value represents its effect on the decision-making of
-the model, increasing or decreasing the likelihood of the prediction.
-//Thus in this example, the `FlightTimeMin` and `DistanceMiles` had the largest
-//negative and positive feature importance values respectively and had the most
-//significant influence on the prediction for this particular data point.
+The sum of the feature importance values for a class (in this example, `false`)
+in this data point approximates the logarithm of its odds
+(or {wikipedia}/Logit[log-odds]).
+////
+//Does this mean the sum of the feature importance values for false in this
+example should equal the logit(p), where p is the class_probability for false?
+//Does this imply that the feature importance value itself is the result of a logit function? Or that we use the function to merely represent the distribution of feature importance values?
+////
+While the probability of a class ranges between 0 and 1, its log-odds range 
+between negative and positive infinity. In {kib}, the decision path for each
+class starts near zero, which represents a class probability of 0.5.
+// Is this true for multi-class classification or just binary classification?
+From there, the feature importance values are added to the decision path. The 
+features with the most significant positive or negative impact appear at the top. 
+Thus in this example, the features related to flight time and distance had the 
+most significant influence on this prediction. This type of information can
+help you to understand how models arrive at their predictions. It can also 
+indicate which aspects of your data set are most influential or least useful
+when you are training and tuning your model.
 
 
 [[flightdata-classification-evaluate]]