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Implement pseudo Huber loss (PseudoHuber) evaluation metric for regression analysis (#58734)

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Przemysław Witek 2020-07-01 13:29:56 +02:00 • committed by GitHub
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docs/java-rest/high-level/ml/evaluate-data-frame.asciidoc
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@ -69,7 +69,8 @@ include-tagged::{doc-tests-file}[{api}-evaluation-regression]
<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> Mean squared logarithmic error
<7> https://en.wikipedia.org/wiki/Coefficient_of_determination[R squared]
<7> https://en.wikipedia.org/wiki/Huber_loss#Pseudo-Huber_loss_function[Pseudo Huber loss]
<8> https://en.wikipedia.org/wiki/Coefficient_of_determination[R squared]
include::../execution.asciidoc[]
@ -126,5 +127,7 @@ include-tagged::{doc-tests-file}[{api}-results-regression]
<2> Fetching the actual mean squared error 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
<5> Fetching pseudo Huber loss metric by name
<6> Fetching the actual pseudo Huber loss value
<7> Fetching R squared metric by name
<8> Fetching the actual R squared value