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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`:::: `analysis`.`classification`.`num_top_feature_importance_values`::::
(Optional, integer) (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`:::: `analysis`.`classification`.`training_percent`::::
(Optional, integer) (Optional, integer)
@ -235,7 +238,10 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name]
`analysis`.`regression`.`num_top_feature_importance_values`:::: `analysis`.`regression`.`num_top_feature_importance_values`::::
(Optional, integer) (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`:::: `analysis`.`regression`.`training_percent`::::
(Optional, integer) (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. to predict then we will report all category probabilities. Defaults to 2.
end::num-top-classes[] 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[] tag::over-field-name[]
The field used to split the data. In particular, this property is used for 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 analyzing the splits with respect to the history of all splits. It is used for