Java Deep Learning Cookbook

How it works...

In step 3, actuals are the actual output for the test input, and predictions are the observed output for the test input.

The evaluation metrics are based on the difference between actuals and predictions. We used ROC evaluation metrics to find this difference. An ROC evaluation is ideal for binary classification problems with datasets that have a uniform distribution of the output classes. Predicting patient mortality is just another binary classification puzzle.

thresholdSteps in the parameterized constructor of ROC is the number of threshold steps to be used for the ROC calculation. When we decrease the threshold, we get more positive values. It increases the sensitivity and means that the neural network will be less confident ...