Class OutlierDetectionParameters
java.lang.Object
co.elastic.clients.elasticsearch.ml.OutlierDetectionParameters
- All Implemented Interfaces:
- JsonpSerializable
@JsonpDeserializable
public class OutlierDetectionParameters
extends Object
implements JsonpSerializable
- See Also:
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Nested Class SummaryNested Classes
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Field SummaryFieldsModifier and TypeFieldDescriptionstatic final JsonpDeserializer<OutlierDetectionParameters>Json deserializer forOutlierDetectionParameters
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Method SummaryModifier and TypeMethodDescriptionfinal BooleanSpecifies whether the feature influence calculation is enabled.final DoubleThe minimum outlier score that a document needs to have in order to calculate its feature influence score.final Stringmethod()The method that outlier detection uses.final IntegerDefines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score.static OutlierDetectionParametersfinal DoubleThe proportion of the data set that is assumed to be outlying prior to outlier detection.voidserialize(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper) Serialize this object to JSON.protected voidserializeInternal(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper) protected static voidsetupOutlierDetectionParametersDeserializer(ObjectDeserializer<OutlierDetectionParameters.Builder> op) final BooleanIftrue, the following operation is performed on the columns before computing outlier scores: (x_i - mean(x_i)) / sd(x_i).toString()
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Field Details- 
_DESERIALIZERJson deserializer forOutlierDetectionParameters
 
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Method Details- 
ofpublic static OutlierDetectionParameters of(Function<OutlierDetectionParameters.Builder, ObjectBuilder<OutlierDetectionParameters>> fn) 
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computeFeatureInfluenceSpecifies whether the feature influence calculation is enabled.API name: compute_feature_influence
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featureInfluenceThresholdThe minimum outlier score that a document needs to have in order to calculate its feature influence score. Value range: 0-1API name: feature_influence_threshold
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methodThe method that outlier detection uses. Available methods arelof,ldof,distance_kth_nn,distance_knn, andensemble. 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
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nNeighborsDefines 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
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outlierFractionThe 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
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standardizationEnabledIftrue, the following operation is performed on the columns before computing outlier scores: (x_i - mean(x_i)) / sd(x_i).API name: standardization_enabled
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serializeSerialize this object to JSON.- Specified by:
- serializein interface- JsonpSerializable
 
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serializeInternal
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toString
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setupOutlierDetectionParametersDeserializerprotected static void setupOutlierDetectionParametersDeserializer(ObjectDeserializer<OutlierDetectionParameters.Builder> op) 
 
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