Training a neural network with neuralnet
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 perception 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.
Getting ready
In this recipe, we will use the iris dataset as our example dataset. We will first split the iris dataset into training and testing datasets, respectively.
How to do it...
Perform the following steps to train a neural network with neuralnet:
- First, load the
irisdataset and split the data into training and testing datasets:
> data(iris)
> ind = sample(2, nrow(iris), replace = TRUE, prob=c(0.7, 0...
