Package co.elastic.clients.elasticsearch.ml

Class DataframeAnalysisOutlierDetection

java.lang.Object
co.elastic.clients.elasticsearch.ml.DataframeAnalysisOutlierDetection
All Implemented Interfaces:
DataframeAnalysisVariant, JsonpSerializable
@JsonpDeserializable
public class DataframeAnalysisOutlierDetection
extends java.lang.Object
implements DataframeAnalysisVariant, JsonpSerializable
See Also:
API specification

  • Field Details

  • Method Details

    • of

      public static DataframeAnalysisOutlierDetection of​(java.util.function.Function<DataframeAnalysisOutlierDetection.Builder,​ObjectBuilder<DataframeAnalysisOutlierDetection>> fn)

    • _dataframeAnalysisKind

      public DataframeAnalysis.Kind _dataframeAnalysisKind()
      DataframeAnalysis variant kind.
      Specified by:
      _dataframeAnalysisKind in interface DataframeAnalysisVariant

    • computeFeatureInfluence

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

      API name: compute_feature_influence

    • featureInfluenceThreshold

      @Nullable public final java.lang.Double featureInfluenceThreshold()
      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

      @Nullable public final java.lang.String method()
      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

      @Nullable public final java.lang.Integer nNeighbors()
      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

      @Nullable public final java.lang.Double outlierFraction()
      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

      @Nullable public final java.lang.Boolean standardizationEnabled()
      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

    • serialize

      public void serialize​(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper)
      Serialize this object to JSON.
      Specified by:
      serialize in interface JsonpSerializable

    • serializeInternal

      protected void serializeInternal​(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper)

    • setupDataframeAnalysisOutlierDetectionDeserializer

      protected static void setupDataframeAnalysisOutlierDetectionDeserializer​(ObjectDeserializer<DataframeAnalysisOutlierDetection.Builder> op)