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
Constructors Constructor Description Builder()
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Method Summary
Modifier and Type Method Description DataframeAnalysisOutlierDetection
build()
Builds aDataframeAnalysisOutlierDetection
.DataframeAnalysisOutlierDetection.Builder
computeFeatureInfluence(java.lang.Boolean value)
Specifies whether the feature influence calculation is enabled.DataframeAnalysisOutlierDetection.Builder
featureInfluenceThreshold(java.lang.Double value)
The minimum outlier score that a document needs to have in order to calculate its feature influence score.DataframeAnalysisOutlierDetection.Builder
method(java.lang.String value)
The method that outlier detection uses.DataframeAnalysisOutlierDetection.Builder
nNeighbors(java.lang.Integer value)
Defines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score.DataframeAnalysisOutlierDetection.Builder
outlierFraction(java.lang.Double value)
The proportion of the data set that is assumed to be outlying prior to outlier detection.protected DataframeAnalysisOutlierDetection.Builder
self()
DataframeAnalysisOutlierDetection.Builder
standardizationEnabled(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)
.Methods inherited from class co.elastic.clients.util.WithJsonObjectBuilderBase
withJson
Methods inherited from class co.elastic.clients.util.ObjectBuilderBase
_checkSingleUse, _listAdd, _listAddAll, _mapPut, _mapPutAll
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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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 java.lang.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 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
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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
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
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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
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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
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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:
java.lang.NullPointerException
- if some of the required fields are null.
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