Categories: r, neural-network

neural net - undefined column selected

1 answer

Data Partition

I have several factor variables so I decided to use dummy variables instead for ANN.

set.seed(222) m <- model.matrix( ~price+month+year+area+larea+otype+cid+rid+did+renovation+nrooms+nbeds+nbaths+nbalcs+wfloor+status, data = data) ind <- sample(2, nrow(m), replace = TRUE, prob = c(0.8, 0.2)) training <- m[ind==1,] testing <- m[ind==2,] 

neural net

n <- neuralnet(price~.,                 data = training,                 hidden = 1,                 err.fct = "sse",                 linear.output = FALSE) 

Error in [.data.frame(data, , model.list$variables) : undefined columns selected

I use dot because I have 94 explanatory variables. when I run price on two explanatory variables to see what the problem was it worked. There is some problem with dot, is it only used for linear regression and I got it wrong? how can I solve this problem?

All answers to this question, which has the identifier 61239006

The best answer:

The model.matrix is a matrix and not a data.frame. According to ?neuralnet

data - a data frame containing the variables specified in formula.


So, we may need to convert to data.frame with as.data.frame as this works correctly with data.frame input

library(neuralnet) data(iris) nn <- neuralnet(Species~ ., iris,          linear.output = FALSE, hidden = 1, err.fct = "sse") nn$weights #[[1]] #[[1]][[1]] #           [,1] #[1,]  -9.900398 #[2,]  -1.527258 #[3,] -13.201669 #[4,]  11.624309 #[5,]  17.896367  #[[1]][[2]] #          [,1]      [,2]      [,3] #[1,]  32.51005 -4.014045 -4.303671 #[2,] -65.72300  4.013259  4.304652 

In the OP's code, the issue is that model.matrix creates column names with nonstandard names. It can be converted to standard names with make.unique

names(training) <- make.names(names(training)) n <- neuralnet(price ~ .,              data = training,             hidden = 1,             err.fct = "sse",        linear.output = FALSE)    n$weights #[[1]] #[[1]][[1]] #              [,1] # [1,]  -0.62625018 # [2,]   1.39124245 # [3,]  -2.59472834 # [4,]   0.27773897 # [5,]   7.15830865 # [6,]  -2.93583230 # ... 

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