134 lines
5.4 KiB
Plaintext
134 lines
5.4 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 {dfanalytics} API
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Evaluates the {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 {dfanalytics} 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> Evaluation to be performed
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==== Evaluation
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Evaluation to be performed.
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Currently, supported evaluations include: +OutlierDetection+, +Classification+, +Regression+.
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===== Outlier detection
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-evaluation-outlierdetection]
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--------------------------------------------------
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<1> Constructing a new evaluation
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<2> 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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<3> 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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<4> The remaining parameters are the metrics to be calculated based on the two fields described above
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<5> {wikipedia}/Precision_and_recall#Precision[Precision] calculated at thresholds: 0.4, 0.5 and 0.6
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<6> {wikipedia}/Precision_and_recall#Recall[Recall] calculated at thresholds: 0.5 and 0.7
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<7> {wikipedia}/Confusion_matrix[Confusion matrix] calculated at threshold 0.5
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<8> {wikipedia}/Receiver_operating_characteristic#Area_under_the_curve[AuC ROC] calculated and the curve points returned
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===== Classification
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-evaluation-classification]
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--------------------------------------------------
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<1> Constructing a new evaluation
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<2> Name of the field in the index. Its value denotes the actual (i.e. ground truth) class the example belongs to.
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<3> Name of the field in the index. Its value denotes the predicted (as per some ML algorithm) class of the example.
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<4> The remaining parameters are the metrics to be calculated based on the two fields described above
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<5> Accuracy
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<6> Precision
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<7> Recall
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<8> Multiclass confusion matrix of size 3
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===== Regression
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-evaluation-regression]
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--------------------------------------------------
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<1> Constructing a new evaluation
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<2> Name of the field in the index. Its value denotes the actual (i.e. ground truth) value for an example.
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<3> Name of the field in the index. Its value denotes the predicted (as per some ML algorithm) value for the example.
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<4> The remaining parameters are the metrics to be calculated based on the two fields described above
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<5> {wikipedia}/Mean_squared_error[Mean squared error]
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<6> Mean squared logarithmic error
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<7> {wikipedia}/Huber_loss#Pseudo-Huber_loss_function[Pseudo Huber loss]
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<8> {wikipedia}/Coefficient_of_determination[R squared]
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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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==== Results
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===== Outlier detection
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-results-outlierdetection]
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--------------------------------------------------
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<1> Fetching precision metric by name
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<2> Fetching precision at a given (0.4) threshold
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<3> Fetching confusion matrix metric by name
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<4> Fetching confusion matrix at a given (0.5) threshold
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===== Classification
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-results-classification]
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--------------------------------------------------
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<1> Fetching accuracy metric by name
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<2> Fetching the actual accuracy value
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<3> Fetching precision metric by name
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<4> Fetching the actual precision value
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<5> Fetching recall metric by name
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<6> Fetching the actual recall value
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<7> Fetching multiclass confusion matrix metric by name
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<8> Fetching the contents of the confusion matrix
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<9> Fetching the number of classes that were not included in the matrix
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===== Regression
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["source","java",subs="attributes,callouts,macros"]
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--------------------------------------------------
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include-tagged::{doc-tests-file}[{api}-results-regression]
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--------------------------------------------------
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<1> Fetching mean squared error metric by name
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<2> Fetching the actual mean squared error value
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<3> Fetching mean squared logarithmic error metric by name
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<4> Fetching the actual mean squared logarithmic error value
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<5> Fetching pseudo Huber loss metric by name
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<6> Fetching the actual pseudo Huber loss value
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<7> Fetching R squared metric by name
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<8> Fetching the actual R squared value
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