The “neuralnet” Function in R

• Package: neuralnet

• Purpose: Train and fit a neural network model.

• General class: Model

• Required argument(s):

• formula: A formula specifying the model structure (e.g., response ~ predictors).

• data: Data frame containing the variables in the formula.

• hidden: A vector specifying the number of neurons in each hidden layer.

• Notable optional arguments:

• linear.output: Boolean indicating if the output should be linear (default is TRUE).

• threshold: A numeric value to set the stopping criterion based on the threshold for error reduction.

• stepmax: Maximum number of steps for the training process.

• learningrate: The learning rate used for training the network.

• Example:

• # Load the required library
  library(neuralnet)
  
  # Example dataset
  data(iris)
  
  # Fit a neural network model
  model <- neuralnet(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris, hidden = c(5, 3), linear.output = FALSE)
  
  # Print the model summary
  print(model)
  
  # Predict on the same dataset
  predictions <- predict(model, iris)
  head(predictions)

• In this example, the neuralnet function is used to fit a neural network model to the iris dataset. The formula specifies that the model should predict Species based on the four features. The hidden argument specifies a network with two hidden layers (5 and 3 neurons). The linear.output = FALSE argument indicates that the output should be a classification. The function outputs the model, and predictions are made on the same dataset.