How does the neuralnet works in R

How does the neuralnet works in R

Recipe Objective - How does the neuralnet() works in R?

neuralnet is a package in R used to train the neural networks using backpropagation, apply resilient backpropagation with or without the weights backtracking. The package allows the flexible settings through custom choice of error and the activation function. Also, the calculation of the generalized weights are implemented. The package was launched in year 2019 and was supported by German Research Foundation. The package contains various functions like compute(), confidence.interval(), gwplot() etc for implementing neural network, building models and plotting them to generate reports.

This recipe explains what is neuralnet package, how it is beneficial for neural network models and how it can be execute

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Explanation of neuralnet.

The compute() function in neuralnet package does the maximum of computation work which takes input "x" as an object of class nn, "covariate" as the dataframe or matrix containing variables that had been used to train neural network, "rep" as the integer indicating neural network’s repetition which should be used. The "compute" returns list of neurons and a matrix containing result of the overall neural network as "net.result" .

The confidence.interval() function in neuralnet package calculates the confidence in the weights of the neural network. It takes input "x" as an object of class nn, "alpha" as a numerical value which is been used to train neural network. The confidence intervals are calculated under assumption of a local identification of a given neural network and if the assumption is violated then results will not be reasonable. The choosen error function should always be equal to the negative log-likelihood function, otherwise results are not meaningfull for the neural network.

The gwplot() function in neuralnet package does the plotting work for generalized weights of the neural network which takes input "x" as an object of class nn, "rep" which is an integer indicating a repetition to plot that is if rep = "best" repetition with smallest error will be plotted and if not stated then all the repetitions will be plotted, "max maximum" that is it is of the y axis in which max is set to the highest y value, "min minimum" that is of the y axis in which min is set to the smallest y value. The confidence.interval() funtion in gwplot() returns lower.ci that is a list containing lower confidence bounds of all weights of neural network differentiated by repetitions, upper.ci that is a list containing upper confidence bounds of all weights of the neural network differentiated by repetitions and nic that is a vector containg information criteria NIC for each and every repetition.

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