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Implement MSLE (MeanSquaredLogarithmicError) evaluation metric for regression analysis (#58684)

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Przemysław Witek 2020-06-30 13:06:15 +02:00 • committed by GitHub
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docs/java-rest/high-level/ml/evaluate-data-frame.asciidoc
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@ -68,7 +68,8 @@ include-tagged::{doc-tests-file}[{api}-evaluation-regression]
<3> Name of the field in the index. Its value denotes the predicted (as per some ML algorithm) value for the example.
<4> The remaining parameters are the metrics to be calculated based on the two fields described above
<5> https://en.wikipedia.org/wiki/Mean_squared_error[Mean squared error]
<6> https://en.wikipedia.org/wiki/Coefficient_of_determination[R squared]
<6> Mean squared logarithmic error
<7> https://en.wikipedia.org/wiki/Coefficient_of_determination[R squared]
include::../execution.asciidoc[]
@ -123,5 +124,7 @@ include-tagged::{doc-tests-file}[{api}-results-regression]
<1> Fetching mean squared error metric by name
<2> Fetching the actual mean squared error value
<3> Fetching R squared metric by name
<4> Fetching the actual R squared value
<3> Fetching mean squared logarithmic error metric by name
<4> Fetching the actual mean squared logarithmic error value
<5> Fetching R squared metric by name
<6> Fetching the actual R squared value