R 应用神经网络和nnet-多类分类_R_Neural Network_Nnet_Multiclass Classification

R 应用神经网络和nnet-多类分类

r neural-network

R 应用神经网络和nnet-多类分类,r,neural-network,nnet,multiclass-classification,R,Neural Network,Nnet,Multiclass Classification,我有一个如下所示的数据框： source <- c('A','B','C','C','D','D','D','D','D','D','D','D','D','D', 'D','D','D','D','D','D') target <- c('A1', 'A2', 'A3','A4','A5','A6','A7','A8','A9','A10','A11','A12','A13','A14','A15','A16','A17','A18','A19','A20') df <- d

我有一个如下所示的数据框：

source <- c('A','B','C','C','D','D','D','D','D','D','D','D','D','D', 'D','D','D','D','D','D')
target <- c('A1', 'A2', 'A3','A4','A5','A6','A7','A8','A9','A10','A11','A12','A13','A14','A15','A16','A17','A18','A19','A20')
df <- data.frame(source, target)

该数据集是415个数据点的子集

我做的第一件事是使用

class.ind

将分类变量转换为虚拟变量。然后从那里我创建了一个神经网络模型，这将有助于预测目标

trainData <- cbind(class.ind(df$source)
trainData <- as.data.frame(trainData)
trainData2 <- cbind(trainData, df$target)
size <- length(unique(trainData2$`df$target`))
m1 <- nnet(`df$target` ~., trainData2, size=size, MaxNWts=10000)

trainData
trainData <- cbind(class.ind(df$source)
trainData <- as.data.frame(trainData)
trainData2 <- cbind(trainData, df$target)
size <- length(unique(trainData2$`df$target`))
m1 <- nnet(`df$target` ~., trainData2, size=size, MaxNWts=10000)