MeanSquaredLogarithmicError (jaicore-ml 0.2.5 API)

java.lang.Object
- ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure<java.lang.Double,org.api4.java.ai.ml.regression.evaluation.IRegressionPrediction>
- - ai.libs.jaicore.ml.regression.loss.dataset.ARegressionMeasure
  - - ai.libs.jaicore.ml.regression.loss.dataset.MeanSquaredLogarithmicError

All Implemented Interfaces:

org.api4.java.ai.ml.core.evaluation.supervised.loss.IDeterministicPredictionPerformanceMeasure<java.lang.Double,org.api4.java.ai.ml.regression.evaluation.IRegressionPrediction>
```
public class MeanSquaredLogarithmicError
extends ARegressionMeasure
```

Constructor Summary

Constructors
Constructor Description

MeanSquaredLogarithmicError()

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`double`	`loss(java.util.List<? extends java.lang.Double> expected, java.util.List<? extends org.api4.java.ai.ml.regression.evaluation.IRegressionPrediction> predicted)`	If this performance measure is originally a score function its score is transformed into a loss by multiplying the score with -1.

Methods inherited from class ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure
averageInstanceWiseLoss, averageInstanceWiseScore, checkConsistency, loss, score, score

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.api4.java.ai.ml.core.evaluation.supervised.loss.IDeterministicPredictionPerformanceMeasure
loss, score, score

- Constructor Detail
  - MeanSquaredLogarithmicError
```
public MeanSquaredLogarithmicError()
```
- Method Detail
  - loss
```
public double loss(java.util.List<? extends java.lang.Double> expected,
                   java.util.List<? extends org.api4.java.ai.ml.regression.evaluation.IRegressionPrediction> predicted)
```
    Description copied from class: APredictionPerformanceMeasure
    
    If this performance measure is originally a score function its score is transformed into a loss by multiplying the score with -1. (loss=-score).
    
    Specified by:
    
    loss in interface org.api4.java.ai.ml.core.evaluation.supervised.loss.IDeterministicPredictionPerformanceMeasure<java.lang.Double,org.api4.java.ai.ml.regression.evaluation.IRegressionPrediction>
    
    Overrides:
    
    loss in class APredictionPerformanceMeasure<java.lang.Double,org.api4.java.ai.ml.regression.evaluation.IRegressionPrediction>