Class DataframeAnalysisOutlierDetection
java.lang.Object
co.elastic.clients.elasticsearch.ml.DataframeAnalysisOutlierDetection
- All Implemented Interfaces:
DataframeAnalysisVariant
,JsonpSerializable
@JsonpDeserializable
public class DataframeAnalysisOutlierDetection
extends Object
implements DataframeAnalysisVariant, JsonpSerializable
- See Also:
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Nested Class Summary
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Field Summary
Modifier and TypeFieldDescriptionstatic final JsonpDeserializer<DataframeAnalysisOutlierDetection>
Json deserializer forDataframeAnalysisOutlierDetection
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Method Summary
Modifier and TypeMethodDescriptionDataframeAnalysis variant kind.final Boolean
Specifies whether the feature influence calculation is enabled.final Double
The minimum outlier score that a document needs to have in order to calculate its feature influence score.final String
method()
The method that outlier detection uses.final Integer
Defines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score.of
(Function<DataframeAnalysisOutlierDetection.Builder, ObjectBuilder<DataframeAnalysisOutlierDetection>> fn) final Double
The proportion of the data set that is assumed to be outlying prior to outlier detection.void
serialize
(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper) Serialize this object to JSON.protected void
serializeInternal
(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper) protected static void
setupDataframeAnalysisOutlierDetectionDeserializer
(ObjectDeserializer<DataframeAnalysisOutlierDetection.Builder> op) final Boolean
If true, the following operation is performed on the columns before computing outlier scores:(x_i - mean(x_i)) / sd(x_i)
.toString()
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
Methods inherited from interface co.elastic.clients.elasticsearch.ml.DataframeAnalysisVariant
_toDataframeAnalysis
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Field Details
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_DESERIALIZER
Json deserializer forDataframeAnalysisOutlierDetection
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Method Details
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of
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_dataframeAnalysisKind
DataframeAnalysis variant kind.- Specified by:
_dataframeAnalysisKind
in interfaceDataframeAnalysisVariant
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computeFeatureInfluence
Specifies whether the feature influence calculation is enabled.API name:
compute_feature_influence
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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
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method
The 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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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
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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
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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
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serialize
Serialize this object to JSON.- Specified by:
serialize
in interfaceJsonpSerializable
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serializeInternal
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toString
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setupDataframeAnalysisOutlierDetectionDeserializer
protected static void setupDataframeAnalysisOutlierDetectionDeserializer(ObjectDeserializer<DataframeAnalysisOutlierDetection.Builder> op)
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