Java Deep Learning Projects

Evaluating the model

Once the training has been completed, the next task would be evaluating the model. We will evaluate the model's performance on the test set. For the evaluation, we will be using Evaluation(); it creates an evaluation object with two possible classes (survived or not survived). More technically, the Evaluation class computes the evaluation metrics such as precision, recall, F1, accuracy, and Matthews' correlation coefficient. The last one is used to evaluate a binary classifier. Now let's take a brief overview on these metrics:

Accuracy is the ratio of correctly predicted samples to total samples:

Precision is the ratio ...