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[DOCS] Clarifies description of num_top_feature_importance_values #52246

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Feb 18, 2020
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[DOCS] Clarifies description of num_top_feature_importance_values #52246

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a72dedb
[DOCS] Clarifies num_top_feature_importance_values
lcawl Feb 12, 2020
e202691
[DOCS] Simplifies property description
lcawl Feb 12, 2020
f6bea5a
[DOCS] Removes unnecessary tagged region
lcawl Feb 13, 2020
7454057
Update docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc
lcawl Feb 14, 2020
e1dcefa
Update docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc
lcawl Feb 14, 2020
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10 changes: 8 additions & 2 deletions docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc
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Expand Up @@ -150,7 +150,10 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed]

`analysis`.`classification`.`num_top_feature_importance_values`::::
(Optional, integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-feature-importance-values]
Advanced configuration option. Specifies the maximum number of
{ml-docs}/dfa-classification.html#dfa-classification-feature-importance[feature
importance] values to return. By default, it is zero and no feature importance
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calculation occurs.

`analysis`.`classification`.`training_percent`::::
(Optional, integer)
Expand Down Expand Up @@ -233,7 +236,10 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name]

`analysis`.`regression`.`num_top_feature_importance_values`::::
(Optional, integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-feature-importance-values]
Advanced configuration option. Specifies the maximum number of
{ml-docs}/dfa-regression.html#dfa-regression-feature-importance[feature importance]
values to return. By default, it is zero and no feature importance calculation
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occurs.

`analysis`.`regression`.`training_percent`::::
(Optional, integer)
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7 changes: 0 additions & 7 deletions docs/reference/ml/ml-shared.asciidoc
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Expand Up @@ -906,13 +906,6 @@ total number of categories (in the {version} version of the {stack}, it's two)
to predict then we will report all category probabilities. Defaults to 2.
end::num-top-classes[]

tag::num-top-feature-importance-values[]
Advanced configuration option. If set, feature importance for the top
most important features will be computed. Importance is calculated
using the SHAP (SHapley Additive 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.].
end::num-top-feature-importance-values[]

tag::over-field-name[]
The field used to split the data. In particular, this property is used for
analyzing the splits with respect to the history of all splits. It is used for
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