Class DataframeAnalysisOutlierDetection.Builder
java.lang.Object
co.elastic.clients.util.ObjectBuilderBase
co.elastic.clients.util.WithJsonObjectBuilderBase<DataframeAnalysisOutlierDetection.Builder>
co.elastic.clients.elasticsearch.ml.DataframeAnalysisOutlierDetection.Builder
- All Implemented Interfaces:
WithJson<DataframeAnalysisOutlierDetection.Builder>
,ObjectBuilder<DataframeAnalysisOutlierDetection>
- Enclosing class:
- DataframeAnalysisOutlierDetection
public static class DataframeAnalysisOutlierDetection.Builder
extends WithJsonObjectBuilderBase<DataframeAnalysisOutlierDetection.Builder>
implements ObjectBuilder<DataframeAnalysisOutlierDetection>
Builder for
DataframeAnalysisOutlierDetection
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptionbuild()
Builds aDataframeAnalysisOutlierDetection
.computeFeatureInfluence
(Boolean value) Specifies whether the feature influence calculation is enabled.featureInfluenceThreshold
(Double value) The minimum outlier score that a document needs to have in order to calculate its feature influence score.The method that outlier detection uses.nNeighbors
(Integer value) Defines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score.outlierFraction
(Double value) The proportion of the data set that is assumed to be outlying prior to outlier detection.self()
standardizationEnabled
(Boolean value) If true, the following operation is performed on the columns before computing outlier scores:(x_i - mean(x_i)) / sd(x_i)
.Methods inherited from class co.elastic.clients.util.WithJsonObjectBuilderBase
withJson
Methods inherited from class co.elastic.clients.util.ObjectBuilderBase
_checkSingleUse, _listAdd, _listAddAll, _mapPut, _mapPutAll
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Constructor Details
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Builder
public Builder()
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Method Details
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computeFeatureInfluence
public final DataframeAnalysisOutlierDetection.Builder computeFeatureInfluence(@Nullable Boolean value) Specifies whether the feature influence calculation is enabled.API name:
compute_feature_influence
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featureInfluenceThreshold
public final DataframeAnalysisOutlierDetection.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
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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
public final DataframeAnalysisOutlierDetection.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
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self
- Specified by:
self
in classWithJsonObjectBuilderBase<DataframeAnalysisOutlierDetection.Builder>
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build
Builds aDataframeAnalysisOutlierDetection
.- Specified by:
build
in interfaceObjectBuilder<DataframeAnalysisOutlierDetection>
- Throws:
NullPointerException
- if some of the required fields are null.
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