Summary

In this chapter, you trained, evaluated, and compared multiple neural network architectures on the GermanCredit and PimaIndiansDiabetes2 classification tasks. To achieve this, you created balanced partitions and folds with the groupdata2 package. You used the neuralnet package to specify and train neural networks and used those trained models to predict the classes in the development and validation sets. Both in theory and by using caret's confusionMatrix function, you learned how to calculate accuracy, precision, recall, and F1 metrics. You implemented a cross-validation training loop and used it to compare multiple model architectures. Finally, we introduced multiclass classification and the softmax function.

If you wish to build more advanced neural networks while keeping the code simple, the keras package would be a good place to start.

In the next chapter, you will learn how to fit and interpret linear and logistic regression models. We will use the cvms package to easily...