How it works...

Now, let's assess the performance of the model on train and test datasets. The AUC on the train data is 0.978 and on the test data is 0.982:

# Train accuracy (AUC)
> train_pred <- predict(model.nn,occupancy_train.x)
> train_yhat <- max.col(t(train_pred))-1
> roc_obj <- pROC::roc(c(occupancy_train.y), c(train_yhat))
> pROC::auc(roc_obj)
Area under the curve: 0.9786

#Test accuracy (AUC)
> test_pred <- predict(nnmodel,occupancy_test.x)
> test_yhat <- max.col(t(test_pred))-1
> roc_obj <- pROC::roc(c(occupancy_test.y), c(test_yhat))
> pROC::auc(roc_obj)
Area under the curve: 0.9824