[DOCS] Add total feature importance to classification example #1382
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lcawl
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| If you chose to calculate {feat-imp}, the destination index also contains | ||
| `ml.feature_importance` objects. Every field that is included in the | ||
| {classanalysis} (known as a _feature_ of the data point) is assigned a {feat-imp} | ||
| value. This value has both a magnitude and a direction (positive or negative), | ||
| which indicates how each field affects a particular prediction. Only the most | ||
| significant values (in this case, the top 10) are stored in the index. However, | ||
| the trained model metadata also contains the average magnitude of the {feat-imp} | ||
| values for each field across all the training data. You can view this |
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Related to elastic/kibana#78238 and elastic/ml-cpp#1387 and #1378 and #1379
This PR adds total feature importance values to the classification example (https://www.elastic.co/guide/en/machine-learning/master/flightdata-classification.html). It also adds an example of a classification job with more than two classes in the feature importance overview (https://www.elastic.co/guide/en/machine-learning/master/ml-feature-importance.html).
It also refreshes some screenshots in the example that were out-dated.
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