machine learning - R: caret and nnet Error with big data -

i've problem. i've dataset lot of features. when try perform nnet caret r give me error. if try perform smaller part of features, nnet converge.

here code:

> dim(traint) [1]  130 3413 > nnfit <- train(target ~ ., data = traint, + method = "nnet", +                trcontrol = fitcontrol#, +                #trcontrol = ctrl, metric = "roc",  +                #verbose = true#, +                #tunegrid = nngrid + ) wrong; accuracy metric values missing:     accuracy       kappa      min.   : na   min.   : na    1st qu.: na   1st qu.: na    median : na   median : na    mean   :nan   mean   :nan    3rd qu.: na   3rd qu.: na    max.   : na   max.   : na    na's   :9     na's   :9     error in train.default(x, y, weights = w, ...) : stopping in addition: there 50 or more warnings (use warnings() see first 50) > > nnfit <- train(target ~ ., data = traint[,1:100],                method = "nnet",                trcontrol = fitcontrol#,                #trcontrol = ctrl, metric = "roc",                 #verbose = true#,                #tunegrid = nngrid ) # weights:  102 initial  value 65.440715  iter  10 value 34.586483 iter  20 value 25.531746 iter  30 value 22.930604 iter  40 value 22.919387 iter  50 value 20.326238 iter  60 value 20.018595 iter  70 value 5.289718 iter  80 value 0.016055 final  value 0.000063  converged # weights:  304 initial  value 85.540457  iter  10 value 25.219303 iter  20 value 5.562977 iter  30 value 4.712105 iter  40 value 4.676887 iter  50 value 4.625627 iter  60 value 4.622304 iter  70 value 4.597801 iter  80 value 4.582877 iter  90 value 4.570602 iter 100 value 4.569542 final  value 4.569542  stopped after 100 iterations [...] initial  value 75.037558  iter  10 value 4.301843 iter  20 value 1.495044 iter  30 value 0.159978 iter  40 value 0.118735 iter  50 value 0.110560 iter  60 value 0.101595 iter  70 value 0.079860 iter  80 value 0.073034 iter  90 value 0.065459 iter 100 value 0.052024 final  value 0.052024  stopped after 100 iterations # weights:  506 initial  value 95.448738  iter  10 value 20.859400 iter  20 value 6.493820 iter  30 value 5.597509 iter  40 value 5.516322 iter  50 value 5.510970 iter  60 value 5.510881 final  value 5.510881  converged 

can me? :)

ps: session info:

> sessioninfo() r version 3.2.0 (2015-04-16) platform: x86_64-w64-mingw32/x64 (64-bit) running under: windows 8 x64 (build 9200)  locale: [1] lc_collate=italian_italy.1252  lc_ctype=italian_italy.1252    [3] lc_monetary=italian_italy.1252 lc_numeric=c                   [5] lc_time=italian_italy.1252      attached base packages: [1] stats     graphics  grdevices utils     datasets  methods   base       other attached packages: [1] nnet_7.3-9      caret_6.0-47    ggplot2_1.0.1   lattice_0.20-31  loaded via namespace (and not attached):  [1] rcpp_0.11.6         magrittr_1.5        splines_3.2.0       mass_7.3-40          [5] munsell_0.4.2       colorspace_1.2-6    foreach_1.4.2       minqa_1.2.4          [9] car_2.0-25          stringr_1.0.0       plyr_1.8.2          tools_3.2.0         [13] parallel_3.2.0      pbkrtest_0.4-2      grid_3.2.0          gtable_0.1.2        [17] nlme_3.1-120        mgcv_1.8-6          quantreg_5.11       e1071_1.6-4         [21] class_7.3-12        iterators_1.0.7     gtools_3.5.0        lme4_1.1-7          [25] digest_0.6.8        matrix_1.2-0        nloptr_1.0.4        reshape2_1.4.1      [29] codetools_0.2-11    stringi_0.4-1       compiler_3.2.0      bradleyterry2_1.0-6 [33] scales_0.2.4        sparsem_1.6         brglm_0.5-9         proto_0.3-10   

edit: forget comma in code :( i'm while col , test.

@cyberj0g:

i try suggested:

1- analyzing summary have seen numbers.

2- if call warning () not return anything, if try stop before completing nnet me:

        > nnfit <- train(target ~ ., data = traint,     +                method = "nnet",     +                trcontrol = fitcontrol#,     +                #trcontrol = ctrl, metric = "roc",      +                #verbose = true#,     +                #tunegrid = nngrid     + )      warning messages:     1: in eval(expr, envir, enclos) :       model fit failed fold1.rep1: size=1, decay=0e+00 error in nnet.default(x, y, w, entropy = true, ...) :        many (3011) weights      2: in eval(expr, envir, enclos) :       model fit failed fold1.rep1: size=3, decay=0e+00 error in nnet.default(x, y, w, entropy = true, ...) :        many (9031) weights      3: in eval(expr, envir, enclos) :       model fit failed fold1.rep1: size=5, decay=0e+00 error in nnet.default(x, y, w, entropy = true, ...) :        many (15051) weights      4: in eval(expr, envir, enclos) :       model fit failed fold1.rep1: size=1, decay=1e-01 error in nnet.default(x, y, w, entropy = true, ...) :        many (3011) weights      5: in eval(expr, envir, enclos) :       model fit failed fold1.rep1: size=3, decay=1e-01 error in nnet.default(x, y, w, entropy = true, ...) :        many (9031) weights 

3- if increase number of cv (if understand refear it) problem same:

    > fitcontrol <- traincontrol(## 5-fold cv +   method = "repeatedcv", +   number = 1000, +   ## repeated 5 times +   repeats = 5) > nnfit <- train(target ~ ., data = traint, +                method = "nnet", +                trcontrol = fitcontrol#, +                #trcontrol = ctrl, metric = "roc",  +                #verbose = true#, +                #tunegrid = nngrid + )  there 50 or more warnings (use warnings() see first 50) 

it's not clear causing error, suggest following:

1. check data abnormalities: summary(traint)
2. check warnings after error: warnings()
3. try increase number of iterations: traincontrol(number=1000)

also, full dataset contains barely enough samples train model 130 predictors (it depends, though). convergence on 100 samples means nothing.