47 lines
2.1 KiB
Plaintext
47 lines
2.1 KiB
Plaintext
--
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:api: evaluate-data-frame
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:request: EvaluateDataFrameRequest
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:response: EvaluateDataFrameResponse
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--
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[role="xpack"]
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[id="{upid}-{api}"]
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=== Evaluate Data Frame API
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The Evaluate Data Frame API is used to evaluate an ML algorithm that ran on a {dataframe}.
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The API accepts an +{request}+ object and returns an +{response}+.
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[id="{upid}-{api}-request"]
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==== Evaluate Data Frame Request
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-request]
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--------------------------------------------------
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<1> Constructing a new evaluation request
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<2> Reference to an existing index
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<3> The query with which to select data from indices
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<4> Kind of evaluation to perform
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<5> Name of the field in the index. Its value denotes the actual (i.e. ground truth) label for an example. Must be either true or false
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<6> Name of the field in the index. Its value denotes the probability (as per some ML algorithm) of the example being classified as positive
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<7> The remaining parameters are the metrics to be calculated based on the two fields described above.
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<8> https://en.wikipedia.org/wiki/Precision_and_recall[Precision] calculated at thresholds: 0.4, 0.5 and 0.6
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<9> https://en.wikipedia.org/wiki/Precision_and_recall[Recall] calculated at thresholds: 0.5 and 0.7
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<10> https://en.wikipedia.org/wiki/Confusion_matrix[Confusion matrix] calculated at threshold 0.5
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<11> https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve[AuC ROC] calculated and the curve points returned
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include::../execution.asciidoc[]
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[id="{upid}-{api}-response"]
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==== Response
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The returned +{response}+ contains the requested evaluation metrics.
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-response]
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--------------------------------------------------
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<1> Fetching all the calculated metrics results
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<2> Fetching precision metric by name
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<3> Fetching precision at a given (0.4) threshold
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<4> Fetching confusion matrix metric by name
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<5> Fetching confusion matrix at a given (0.5) threshold |