Question

使用nnet包构建模型时遇到问题。如果我理解正确，SIZE参数是隐藏层中的神经元数量。我使用size = 2或1，但这给了我不好的结果。我尝试3次以上，这给了我一个错误：

   Error in (n1 + 1L):n2 : NA/NaN-agument
    Warning
    In nominalTrainWorkflow(dat = trainData, info = trainInfo, method = method,  :
    There were missing values in resampled performance measures.



net <- train(Y~., data=af,
              method = 'nnet',
              preProcess = NULL,
              trControl = fitControl,
              verbose = FALSE,
              tuneGrid=data.frame(.size=2, .decay=5e-3),
              MaxNWts=8000,
              linout =T,
              maxit=1000
   )

那么我做错了什么，如何在没有错误的情况下增加size参数？我有338个观测值和2830个变量的数据。

提前谢谢

Answer 1

download.file(url= "https://cloud.mail.ru/public/6e188e2baa1f%2Fdata.RData",
                    destfile = "data.RData")
load("C:\\Users\\jmiller\\Downloads\\data.RData")
require(nnet)
require(caret)

set.seed(1)
set.seed(123)
seeds <- vector(mode = "list", length = 51)
for(i in 1:50) seeds[[i]] <- sample.int(1000, 22)

fitControl <- trainControl(method = "adaptive_cv", 
                                    repeats = 5,
                                    verboseIter = TRUE,
                                    seeds = seeds)

net <- train(Y~., data=af,
             method = 'nnet',
             preProcess = NULL,
             trControl = fitControl,
             verbose = FALSE,
             tuneGrid=data.frame(.size=3, .decay=5e-3),
             MaxNWts=4000,
             linout =T,
             maxit=1000,
             na.action = "na.omit"
)

问题的根源是自由度。

您的数据对象af具有以下维度：

> dim(af)
[1]  338 2830

并且您在对338的Y观测值中回归所有2,829列（即2,830 - 1），由于自由度不足而无效。

您的模型失败并显示两条消息：

model fit failed...Error in nnet.default(x, y, w, ...) : too many (8494) weights
There were missing values in resampled performance measures.

和你一样，起初我专注于＃2。我尝试将na.action = "na.omit"）参数添加到caret的{{1}}函数中，这是一个好主意但不能解决此问题。然后我意识到第一个信息更重要。它告诉你同样的事情 - 基于解释变量数量的权重数量太高了。

所以我拿走你所拥有的并减少了回归数（使用的列数），现在它工作正常： X＆lt; -af [，1：10]

train()

请注意，我选择了> net <- train(y=af$Y, x=X, + method = 'nnet', + preProcess = NULL, + trControl = fitControl, + verbose = FALSE, + tuneGrid=data.frame(.size=3, .decay=5e-3), + MaxNWts=4000, + linout =T, + maxit=1000, + na.action = "na.omit" + ) # weights: 37 initial value 11680.837531 iter 10 value 567.695784 iter 20 value 536.975033 iter 30 value 531.229395 iter 40 value 529.138440 iter 50 value 528.186846 iter 60 value 527.256175 iter 70 value 526.785917 iter 80 value 524.733350 iter 90 value 523.327685 iter 100 value 523.153812 iter 110 value 522.866651 iter 120 value 522.520324 iter 130 value 519.557709 iter 140 value 519.034003 iter 150 value 518.865745 iter 160 value 518.817728 iter 170 value 518.782720 iter 180 value 518.733853 iter 190 value 518.676881 iter 200 value 518.664181 iter 210 value 518.657738 iter 220 value 518.656348 iter 230 value 518.641653 iter 240 value 518.634891 iter 250 value 518.633536 iter 260 value 518.632945 iter 270 value 518.632559 iter 280 value 518.632273 final value 518.632207 converged # weights: 37 initial value 17038.362932 iter 10 value 501.607084 iter 20 value 492.283099 iter 30 value 491.783153 iter 40 value 490.882435 iter 50 value 490.509487 iter 60 value 490.347638 iter 70 value 490.276362 iter 80 value 490.135871 iter 90 value 490.046862 iter 100 value 490.042117 iter 110 value 490.040552 iter 120 value 490.038263 iter 130 value 490.033484 final value 490.031813 converged # weights: 37 initial value 14383.923273 iter 10 value 689.694268 iter 20 value 632.520128 iter 30 value 506.806825 iter 40 value 497.204789 iter 50 value 496.058566 iter 60 value 495.153170 iter 70 value 493.646931 iter 80 value 492.361818 iter 90 value 492.088474 iter 100 value 492.008913 iter 110 value 491.498147 iter 120 value 491.345332 iter 130 value 491.306452 iter 140 value 491.290815 iter 150 value 491.278200 iter 160 value 491.244056 iter 170 value 491.232009 iter 180 value 491.226179 iter 190 value 491.212958 iter 200 value 491.209201 iter 210 value 491.208694 final value 491.208581 converged # weights: 37 initial value 11084.289612 iter 10 value 622.044592 iter 20 value 520.053760 iter 30 value 507.456841 iter 40 value 502.000020 iter 50 value 497.723332 iter 60 value 495.164695 iter 70 value 494.157722 iter 80 value 492.678614 iter 90 value 491.936464 iter 100 value 491.706778 iter 110 value 491.360232 iter 120 value 491.141080 iter 130 value 490.920068 iter 140 value 490.714486 iter 150 value 490.578641 iter 160 value 490.542580 iter 170 value 490.524909 iter 180 value 490.486804 iter 190 value 490.338614 iter 200 value 490.110047 iter 210 value 489.915521 iter 220 value 489.812929 iter 230 value 489.756234 iter 240 value 489.718024 iter 250 value 489.711636 iter 260 value 489.702732 iter 270 value 489.661668 iter 280 value 489.644434 iter 290 value 489.633946 iter 300 value 489.625405 iter 310 value 489.593120 iter 320 value 489.572627 iter 330 value 489.569740 iter 340 value 489.528552 iter 350 value 489.511607 iter 360 value 489.508993 final value 489.508289 converged # weights: 37 initial value 16479.112645 iter 10 value 615.081555 iter 20 value 501.104433 iter 30 value 481.204462 iter 40 value 479.305649 iter 50 value 477.681071 iter 60 value 476.668767 iter 70 value 476.010448 iter 80 value 475.480687 iter 90 value 474.542118 iter 100 value 473.701240 iter 110 value 473.345255 iter 120 value 473.099798 iter 130 value 472.974764 iter 140 value 472.849499 iter 150 value 472.685356 iter 160 value 472.521386 iter 170 value 472.438417 iter 180 value 472.294666 iter 190 value 472.250269 iter 200 value 472.231414 iter 210 value 472.226611 iter 220 value 472.222344 iter 230 value 472.213415 iter 240 value 472.208953 iter 250 value 472.204120 iter 260 value 472.199380 iter 270 value 472.196929 iter 280 value 472.195935 iter 290 value 472.195052 iter 300 value 472.194005 iter 310 value 472.193293 final value 472.193152 converged # weights: 37 initial value 16669.126242 iter 10 value 888.977047 iter 20 value 637.124939 iter 30 value 624.802902 iter 40 value 622.441515 iter 50 value 618.396731 iter 60 value 618.219093 iter 70 value 617.815129 iter 80 value 617.295605 iter 90 value 616.955292 iter 100 value 616.817402 iter 110 value 616.766023 iter 120 value 616.749874 iter 130 value 616.745140 iter 140 value 616.724481 iter 150 value 616.706785 iter 160 value 616.696147 iter 170 value 616.516945 iter 180 value 616.288825 iter 190 value 615.986669 iter 200 value 615.853741 iter 210 value 615.759414 iter 220 value 615.704034 iter 230 value 615.669519 iter 240 value 615.642135 iter 250 value 615.626347 iter 260 value 615.548658 iter 270 value 615.526684 iter 280 value 615.509073 iter 290 value 615.494325 iter 300 value 615.481876 iter 310 value 615.462706 iter 320 value 615.459488 iter 330 value 615.450263 iter 340 value 615.438322 iter 350 value 615.430339 iter 360 value 615.423634 iter 370 value 615.418235 iter 380 value 615.391565 iter 390 value 615.383094 iter 400 value 615.379039 iter 410 value 615.367349 iter 420 value 615.361426 iter 430 value 615.358651 final value 615.358194 converged >中任意使用的列。您可以（并且应该）根据其性能选择列，除非您有一些理论框架来指导您的选择。例如，您可以使用逐步或套索回归方法选择列，或者通过手动运行各种训练模型并将其性能与ROC曲线（x中提供）进行比较。

当使用SIZE＆gt; 2时使用R“nnet”包的NAs的神经网络

1 个答案: