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This article shows you what the artificial neural network in the advanced algorithm of R language is like, the content is concise and easy to understand, it can definitely brighten your eyes. I hope you can get something through the detailed introduction of this article.

1. Principle analysis of artificial neural network:

Neural network is an operational model, which consists of a large number of nodes (or neurons) and the interconnections between them. Each node represents a specific output function, which is called activation function. The connection between each two nodes represents a weight for the signal passing through the connection, which is called weight, which is equivalent to the memory of the artificial neural network. The output of the network varies according to the network connection mode / weight value and incentive function.

two。 Application in R language

In the artificial neural network (Artificial Neural Network) algorithm, we mainly use the nnet packet

Nnet (formula,data,weights,size,...,subset,na.action,contrasts=NULL)

Function.

3. Discriminant analysis with iris dataset as an example

1) apply the model and observe the output

Fit_nnet=nnet (Species~.,data=iris,size=4,decay=5e-4,maxit=200)

Fit_ NetNet [1: length (fit_nnet)]

3) testing the accuracy of the model

Predict (fit_nnet,iris [, 1:4], type= "class")

Table (iris$Species,predict (fit_nnet,iris [, 1:4], type= "class"))

The above content is what the artificial neural network in the advanced algorithm of R language is like. Have you learned the knowledge or skills? If you want to learn more skills or enrich your knowledge reserve, you are welcome to follow the industry information channel.

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