The neural network is constructed with an interconnected group of nodes, which involves the input, connected weights, processing element, and output. Neural networks can be applied to many areas, such as classification, clustering, and prediction. To train a neural network in R, you can use neuralnet, which is built to train multilayer perceptron in the context of regression analysis, and contains many flexible functions to train forward neural networks. In this recipe, we will introduce how to use neuralnet to train a neural network.
In this recipe, we will use an
iris dataset as our example dataset. We will first split the
iris dataset into a training and testing datasets, respectively.