R Deep Learning Cookbook

How it works...

The performance of the model can be assessed using many metrics such as accuracy, Area under curve (AUC), misclassification error (%), misclassification error count, F1-score, precision, recall, specificity, and so on. However, in this chapter, the assessment of model performance is based on AUC.

The following is the training and cross validation accuracy of the trained model:

# Training accuracy (AUC)> occupancy_train.glm@model$training_metrics@metrics$AUC[1] 0.994583# Cross validation accuracy (AUC)> occupancy_train.glm@model$cross_validation_metrics@metrics$AUC[1] 0.9945057

Now, let's assess the performance of the model on test data. The following code helps in predicting the outcome of the test data:

 # Predict on test ...