Package co.elastic.clients.elasticsearch.ml

Class DataframeAnalysisOutlierDetection.Builder

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

  • Constructor Details

    • Builder

      public Builder()

  • Method Details

    • computeFeatureInfluence

      public final DataframeAnalysisOutlierDetection.Builder computeFeatureInfluence​(@Nullable java.lang.Boolean value)
      Specifies whether the feature influence calculation is enabled.

      API name: compute_feature_influence

    • featureInfluenceThreshold

      public final DataframeAnalysisOutlierDetection.Builder featureInfluenceThreshold​(@Nullable java.lang.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 DataframeAnalysisOutlierDetection.Builder method​(@Nullable java.lang.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 DataframeAnalysisOutlierDetection.Builder nNeighbors​(@Nullable java.lang.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 DataframeAnalysisOutlierDetection.Builder outlierFraction​(@Nullable java.lang.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 DataframeAnalysisOutlierDetection.Builder standardizationEnabled​(@Nullable java.lang.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

    • build

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