Machine Learning with R Cookbook - Second Edition

How it works...

In this recipe, we demonstrate how to predict labels based on a model trained by neuralnet. Initially, we use the compute function to create an output probability matrix based on the trained neural network and the testing dataset. Then, to convert the probability matrix to class labels, we use the which.max function to determine the class label by selecting the column with the maximum probability within the row. Next, we use a table to generate a classification matrix based on the labels of the testing dataset and the predicted labels. As we have created the classification table, we can employ a confusion matrix to measure the prediction performance of the built neural network.