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[DOCS] Clarifies description of num_top_feature_importance_values (#52246) 

Co-Authored-By: Valeriy Khakhutskyy <1292899+valeriy42@users.noreply.github.com>
This commit is contained in:
Lisa Cawley 2020-02-18 08:48:24 -08:00 committed by lcawl
parent 7fcd997b39
commit 123b3c6f55
2 changed files with 8 additions and 9 deletions
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10
docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc
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@ -152,7 +152,10 @@ 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 per document to return. By default, it is zero and no feature importance
calculation occurs.
`analysis`.`classification`.`training_percent`::::
(Optional, integer)
@ -235,7 +238,10 @@ 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 per document to return. By default, it is zero and no feature importance calculation
occurs.
`analysis`.`regression`.`training_percent`::::
(Optional, integer)

7
docs/reference/ml/ml-shared.asciidoc
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@ -904,13 +904,6 @@ 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