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[ML] Additional outlier detection parameters (#47600)

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Adds the following parameters to `outlier_detection`:

- `compute_feature_influence` (boolean): whether to compute or not
   feature influence scores
- `outlier_fraction` (double): the proportion of the data set assumed
   to be outlying prior to running outlier detection
- `standardization_enabled` (boolean): whether to apply standardization
   to the feature values
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Dimitris Athanasiou 2019-10-07 15:28:21 +03:00 • committed by GitHub
parent 924b298259
commit e99435a7f6
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16 changed files with 562 additions and 68 deletions
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docs/java-rest/high-level/ml/put-data-frame-analytics.asciidoc
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@ -96,6 +96,10 @@ include-tagged::{doc-tests-file}[{api}-outlier-detection-customized]
<1> Constructing a new OutlierDetection object
<2> The method used to perform the analysis
<3> Number of neighbors taken into account during analysis
<4> The min `outlier_score` required to compute feature influence
<5> Whether to compute feature influence
<6> The proportion of the data set that is assumed to be outlying prior to outlier detection
<7> Whether to apply standardization to feature values
===== Regression