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R中神经网络的预测

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我想获得神经网络的预测结果的准确性或 RMSE . 我开始使用混淆矩阵，但如前面的答案所示，混淆矩阵为非连续变量提供有效结果 .

有什么方法可以获得神经网络预测的准确度或错误率吗？

作为一个例子，这是我到目前为止的代码：

library(nnet)
library(caret)
library(e1071)
data(rock)
newformula <- perm ~ area + peri + shape
y <- rock[, "perm"]
x <- rock[!colnames(rock)%in% "perm"]
original <- datacol(rock,"perm")

nnclas_model <- nnet(newformula, data = rock, size = 4, decay = 0.0001, maxit = 500)    
nnclas_prediction <- predict(nnclas_model, x)
nnclas_tab <- table(nnclas_prediction, y)
rmse <- sqrt(mean((original - nnclas_prediction)^2))

有谁知道我怎么能做这个工作？或者我如何获得准确度或神经网络预测？任何帮助将深表感谢 .

r confusion-matrix nnet

2 回答

  • 1

    正如评论中所提到的，混淆矩阵用于分类问题 . 如果您打算根据其级别对 perm 进行分类，那么以下代码应该适合您 .

    library(nnet)
library(caret)
library(e1071)
data(rock)
rock$perm <- as.factor(rock$perm)
nnclas_model <- nnet(perm ~ area + peri + shape, data = rock, 
                     size = 4, decay = 0.0001, maxit = 500)
x <- rock[, 1:3]
y <- rock[, 4]
yhat <- predict(nnclas_model, x, type = 'class')
confusionMatrix(as.factor(yhat), y)

    如果你的意思是将 perm 视为连续的，那么混淆矩阵没有任何意义 . 您应该考虑均方误差 .

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  • 2

    我不知道"nnet"，但我使用了"neuralnet"库并且能够获得RMSE . 这是我的完整代码： Just change the data for training_Data and testing_Data as per your requirements and in place of "Channel" give what is your classification attribute

    dat <- read.csv("Give path of your data file here")
summary(dat)
cleandata <- dat
cleandata <- na.omit(cleandata)

#scaling

apply(cleandata,MARGIN = 2, FUN = function(x)sum(is.na(x)))
maxs    =   apply(cleandata,    MARGIN  =   2,  max)
mins    =   apply(cleandata,    MARGIN  =   2,  min)
scaledData =     as.data.frame(scale(cleandata, center  =   mins,   scale   =   maxs    - mins))
summary(scaledData)

#Splitting data in 80:20 ratio
train = sample(1:nrow(scaledData), nrow(scaledData)*0.8)
test = -train
training_Data = scaledData[train,]
testing_Data = scaledData[test,]
dim(training_Data)
dim(testing_Data)

#neural net

library(neuralnet)
n   <- names(training_Data)
f   <- as.formula(paste("Channel    ~", paste(n[!n  %in%    "Channel"], collapse    =   "   +   ")))
neuralnet_Model <- neuralnet(f,data = training_Data, hidden = c(2,1))
plot(neuralnet_Model)
neuralnet_Model$result.matrix
pred_neuralnet<-compute(neuralnet_Model,testing_Data[,2:8])
pred_neuralnet.scaled   <- pred_neuralnet$net.result *(max(scaledData$Channel)-min(scaledData$Channel))+min(scaledData$Channel)
real.values <- (testing_Data$Channel)*(max(cleandata$Channel)-min(cleandata$Channel))+min(cleandata$Channel)
MSE.neuralnetModel  <- sum((real.values - pred_neuralnet.scaled)^2)/nrow(testing_Data)
MSE.neuralnetModel
plot(real.values, pred_neuralnet.scaled, col='red',main='Real   vs  predicted',pch=18,cex=0.7)
abline(0,1,lwd=2)
legend('bottomright',legend='NN',pch=18,col='red',  bty='n')
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