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[ML] Implement AucRoc metric for classification - HLRC (#62304)

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Przemysław Witek 2020-09-30 10:53:45 +02:00 • committed by GitHub
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docs/java-rest/high-level/ml/evaluate-data-frame.asciidoc
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@ -51,11 +51,13 @@ include-tagged::{doc-tests-file}[{api}-evaluation-classification]
<1> Constructing a new evaluation
<2> Name of the field in the index. Its value denotes the actual (i.e. ground truth) class the example belongs to.
<3> Name of the field in the index. Its value denotes the predicted (as per some ML algorithm) class of the example.
<4> The remaining parameters are the metrics to be calculated based on the two fields described above
<5> Accuracy
<6> Precision
<7> Recall
<8> Multiclass confusion matrix of size 3
<4> Name of the field in the index. Its value denotes the array of top classes. Must be nested.
<5> The remaining parameters are the metrics to be calculated based on the two fields described above
<6> Accuracy
<7> Precision
<8> Recall
<9> Multiclass confusion matrix of size 3
<10> {wikipedia}/Receiver_operating_characteristic#Area_under_the_curve[AuC ROC] calculated for class "cat" treated as positive and the rest as negative
===== Regression
@ -115,6 +117,9 @@ include-tagged::{doc-tests-file}[{api}-results-classification]
<7> Fetching multiclass confusion matrix metric by name
<8> Fetching the contents of the confusion matrix
<9> Fetching the number of classes that were not included in the matrix
<10> Fetching AucRoc metric by name
<11> Fetching the actual AucRoc score
<12> Fetching the number of documents that were used in order to calculate AucRoc score
===== Regression