Package co.elastic.clients.elasticsearch.ml

Class OutlierDetectionParameters.Builder

java.lang.Object
co.elastic.clients.util.ObjectBuilderBase
co.elastic.clients.util.WithJsonObjectBuilderBase<OutlierDetectionParameters.Builder>
co.elastic.clients.elasticsearch.ml.OutlierDetectionParameters.Builder
All Implemented Interfaces:
WithJson<OutlierDetectionParameters.Builder>, ObjectBuilder<OutlierDetectionParameters>
Enclosing class:
OutlierDetectionParameters
public static class OutlierDetectionParameters.Builder extends WithJsonObjectBuilderBase<OutlierDetectionParameters.Builder> implements ObjectBuilder<OutlierDetectionParameters>
Builder for OutlierDetectionParameters.

  • Constructor Details

    • Builder

      public Builder()

  • Method Details

    • computeFeatureInfluence

      public final OutlierDetectionParameters.Builder computeFeatureInfluence(@Nullable Boolean value)
      Specifies whether the feature influence calculation is enabled.

      API name: compute_feature_influence

    • featureInfluenceThreshold

      public final OutlierDetectionParameters.Builder featureInfluenceThreshold(@Nullable Double value)
      The minimum outlier score that a document needs to have in order to calculate its feature influence score. Value range: 0-1

      API name: feature_influence_threshold

    • method

      public final OutlierDetectionParameters.Builder method(@Nullable String value)
      The method that outlier detection uses. Available methods are lof, ldof, distance_kth_nn, distance_knn, and ensemble. The default value is ensemble, which means that outlier detection uses an ensemble of different methods and normalises and combines their individual outlier scores to obtain the overall outlier score.

      API name: method

    • nNeighbors

      public final OutlierDetectionParameters.Builder nNeighbors(@Nullable Integer value)
      Defines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score. When the value is not set, different values are used for different ensemble members. This default behavior helps improve the diversity in the ensemble; only override it if you are confident that the value you choose is appropriate for the data set.

      API name: n_neighbors

    • outlierFraction

      public final OutlierDetectionParameters.Builder outlierFraction(@Nullable Double value)
      The proportion of the data set that is assumed to be outlying prior to outlier detection. For example, 0.05 means it is assumed that 5% of values are real outliers and 95% are inliers.

      API name: outlier_fraction

    • standardizationEnabled

      public final OutlierDetectionParameters.Builder standardizationEnabled(@Nullable Boolean value)
      If true, the following operation is performed on the columns before computing outlier scores: (x_i - mean(x_i)) / sd(x_i).

      API name: standardization_enabled

    • self

      protected OutlierDetectionParameters.Builder self()
      Specified by:
      self in class WithJsonObjectBuilderBase<OutlierDetectionParameters.Builder>

    • build

      public OutlierDetectionParameters build()
      Builds a OutlierDetectionParameters.
      Specified by:
      build in interface ObjectBuilder<OutlierDetectionParameters>
      Throws:
      NullPointerException - if some of the required fields are null.