### Question:
LogLossEvaluator extends AbstractClassificationEvaluator<Double> { public static double logLoss(boolean actual, double predicted) { double y = actual ? 1 : 0; double p_binned = Math.max(Math.min(predicted, 1 - EPSILON), EPSILON); return -y * Math.log(p_binned); }  @Override Double evaluate(Classifier<M> classifier, M model, Dataset data); static double logLoss(boolean actual, double predicted);  }### Answer:
@Test public void testCalcLogLoss() { assertEquals(0.69315, LogLossEvaluator.logLoss(true, 0.5), DELTA); assertEquals(0.10536, LogLossEvaluator.logLoss(true, 0.9), DELTA); assertEquals(2.3026, LogLossEvaluator.logLoss(true, 0.1), DELTA); }