在插入符号包中使用朴素贝叶斯分类器时的警告 [英] Warnings while using the Naive Bayes Classifier in the Caret Package
本文介绍了在插入符号包中使用朴素贝叶斯分类器时的警告的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在尝试在插入符号包中运行一个称为Naive Bayes的受监督机器学习分类器。我的数据称为LDA.scores，具有两个分类因子 V4和 G8，以及12个预测变量。我正在使用的代码是由我自己提供的代码在堆栈溢出时由一个好心人改编的（请参阅下面的链接）。该代码确实有效，但是只使用了9个预测变量，而不是数据集中的12个预测变量。当我尝试使用总数据集[2:13]训练Naive Bayes模型时，代码失败。我的下一步是使用数据集中的列的子集（例如[2：9]和[2:10]）系统地运行代码，这绝对好。但是，当我尝试使用列[2:11]（即列标题修饰）来训练模型时，这是发生警告消息的时间。我不明白为什么该代码适用于列[2:10]，但之后却失败（请参阅下面的代码，预热消息和数据）。如果有人可以解决此问题，或者可以通过帮助我理解这些警告消息来提供帮助，那么您的言语将无法表达我的感激之情。我已使用此数据（如下）进行各种分析，但没有任何问题。再次感谢您的帮助。
I am attempting to run a supervised machine learning classifier known as Naive Bayes in the caret Package. My data is called LDA.scores, and has two categorical factors called "V4" and "G8", and 12 predictor variables. The code that I am using was adapted by a kind person on stack overflow from code supplied by myself (see link below).The code does work, however, only 9 predictors were used instead of the 12 predictors in the data-set. When I tried to train the Naive Bayes model with the total data set [2:13], the code failed. My next step was to systematically run the code with a subset of columns from the data-set such as [2:9] and [2:10], which was absolutely fine. However, when I tried to train the model with columns [2:11] (i.e. column heading grooming), this is when the warning messages occurred. I do not understand why the code works for columns [2:10] but fails afterwards (please see the code, warming messages, and data below). If anyone has a solution to this problem or can assist by helping me understand these warning messages, then words cannot express my gratitude. I have used this data (below) for different kinds of analyses and I have not had any issues. Thank you again if you can help.
Naive Bayes Classification task with ROC curve
library(MASS)
library(caret)
set.seed(1234)
构建模型
Build Model
nb_tune <- data.frame(usekernel =TRUE, fL = 0)
nb_mod <- train(x = mydat_resampled[, 2:13], y = as.factor(mydat_resampled[, 1]), method = "nb", trControl = trainControl(method = "cv", classProbs = TRUE), tuneGrid = nb_tune)
警告消息
Warning Messages
Warning messages:
1: In FUN(X[[i]], ...) :
Numerical 0 probability for all classes with observation 3
2: In FUN(X[[i]], ...) :
Numerical 0 probability for all classes with observation 3
模型预测
Model Prediction
nb_pred <- predict(nb_mod, newdata = mydat[ , 2:13], type = "prob")
mydat['nb_pred'] <- nb_pred["G8"]
DATA
DATA
Family Swimming Not.Swimming Running Not.Running
1 v4 -0.48055680 -0.086292700 -0.157157188 -0.438809944
2 v4 0.12600625 -0.074481895 0.057316151 -0.539013927
3 v4 0.06823834 -0.056765686 0.064711783 -0.539013927
4 v4 0.67480139 -0.050860283 0.153459372 -0.539013927
5 v4 0.64591744 -0.050860283 0.072107416 -0.472211271
6 v4 0.21265812 -0.068576492 0.057316151 -0.071395338
7 v4 -0.01841352 -0.068576492 -0.053618335 -0.071395338
8 v4 0.12600625 0.055436970 0.012942357 0.296019267
9 v4 -0.22060120 0.114491000 -0.038827070 0.563229889
10 v4 0.27042603 -0.021333268 0.049920519 -0.037994010
11 v4 0.03935439 -0.044954880 0.012942357 0.195815284
12 v4 -0.45167284 0.008193747 -0.075805232 -0.171599321
13 v4 -0.04729748 -0.056765686 0.035129254 -0.305204632
14 v4 -0.10506539 0.008193747 -0.046222702 0.062209973
15 v4 0.09712230 0.037720761 0.109085578 -0.104796666
16 v4 -0.07618143 0.014099150 -0.038827070 0.095611301
17 v4 0.29930998 0.108585597 0.057316151 0.028808645
18 v4 0.01047043 -0.074481895 0.020337989 -0.071395338
19 v4 -0.24948516 0.002288344 0.035129254 0.329420595
20 v4 -0.04729748 0.049531567 0.057316151 0.296019267
21 v4 -0.01841352 0.043626164 0.005546724 -0.171599321
22 v4 -0.19171725 0.049531567 -0.016640173 -0.071395338
23 v4 -0.48055680 0.020004552 -0.142365923 0.596631217
24 v4 0.01047043 0.008193747 0.220020063 0.062209973
25 v4 -0.42278889 0.025909955 -0.149761556 0.028808645
26 v4 -0.45167284 0.031815358 -0.134970291 -0.138197994
27 v4 -0.30725307 0.049531567 0.042524886 0.095611301
28 v4 0.24154207 -0.039049477 0.072107416 -0.104796666
29 v4 1.45466817 -0.003617059 0.064711783 0.296019267
30 v4 -0.01841352 0.002288344 0.020337989 0.028808645
31 G8 0.38596185 0.084963985 0.049920519 -0.037994010
32 G8 0.15489021 -0.080387298 0.020337989 -0.338605960
33 G8 -0.04729748 0.067247776 0.138668107 0.129012629
34 G8 0.27042603 0.031815358 0.049920519 0.195815284
35 G8 -0.07618143 0.037720761 0.020337989 -0.037994010
36 G8 -0.10506539 0.025909955 -0.083200864 0.396223251
37 G8 -0.01841352 0.126301805 -0.024035805 0.362821923
38 G8 0.01047043 0.031815358 -0.016640173 -0.138197994
39 G8 0.06823834 0.037720761 -0.038827070 0.262617940
40 G8 -0.16283329 -0.050860283 -0.038827070 -0.405408616
41 G8 -0.01841352 -0.039049477 0.005546724 -0.205000649
42 G8 -0.39390493 -0.003617059 -0.090596497 0.129012629
43 G8 -0.04729748 0.008193747 -0.009244540 0.195815284
44 G8 0.01047043 -0.039049477 -0.016640173 -0.205000649
45 G8 0.01047043 -0.003617059 -0.075805232 -0.004592683
46 G8 0.06823834 0.008193747 -0.090596497 -0.205000649
47 G8 -0.04729748 0.014099150 0.012942357 -0.071395338
48 G8 -0.22060120 -0.015427865 -0.075805232 -0.171599321
49 G8 -0.16283329 0.020004552 -0.061013967 -0.104796666
50 G8 -0.07618143 0.031815358 -0.038827070 -0.138197994
51 G8 -0.22060120 0.020004552 -0.112783394 -0.104796666
52 G8 -0.19171725 -0.033144074 -0.068409599 -0.071395338
53 G8 -0.16283329 -0.039049477 -0.090596497 -0.104796666
54 G8 -0.22060120 -0.009522462 -0.053618335 -0.037994010
55 G8 -0.13394934 -0.003617059 -0.075805232 -0.004592683
56 G8 -0.27836911 -0.044954880 -0.090596497 -0.238401977
57 G8 -0.04729748 -0.050860283 0.064711783 0.028808645
58 G8 0.01047043 -0.044954880 0.012942357 -0.305204632
59 G8 0.12600625 -0.068576492 0.042524886 -0.305204632
60 G8 0.06823834 -0.033144074 -0.061013967 -0.271803305
61 G8 0.06823834 -0.027238671 -0.061013967 -0.037994010
62 G8 0.32819394 -0.068576492 0.064711783 -0.372007288
63 G8 0.32819394 0.014099150 0.175646269 0.095611301
64 G8 -0.27836911 0.002288344 -0.068409599 0.195815284
65 G8 0.18377416 0.025909955 0.027733621 0.162413956
66 G8 0.55926557 -0.009522462 0.042524886 0.229216612
67 G8 -0.19171725 -0.009522462 -0.038827070 0.229216612
68 G8 -0.19171725 0.025909955 -0.009244540 0.396223251
69 G8 0.01047043 0.155828820 0.027733621 0.630032545
70 G8 -0.19171725 0.002288344 -0.031431438 0.463025906
71 G8 -0.01841352 -0.044954880 -0.046222702 0.496427234
72 G8 -0.07618143 -0.015427865 -0.031431438 0.062209973
73 G8 -0.13394934 0.008193747 -0.068409599 -0.071395338
74 G8 -0.39390493 0.037720761 -0.120179026 0.229216612
75 G8 -0.04729748 0.008193747 0.035129254 -0.071395338
76 G8 -0.27836911 -0.015427865 -0.061013967 -0.071395338
77 G8 0.70368535 -0.056765686 0.397515240 -0.205000649
78 G8 0.29930998 0.079058582 0.138668107 0.229216612
79 G8 -0.13394934 -0.056765686 0.020337989 -0.305204632
80 G8 0.21265812 0.025909955 0.035129254 0.396223251
Family Fighting Not.Fighting Resting Not.Resting
1 v4 -0.67708172 -0.097624192 0.01081204879 -0.770462870
2 v4 -0.58224128 -0.160103675 -0.03398160805 0.773856776
3 v4 -0.11436177 -0.092996082 0.05710879700 -2.593072768
4 v4 -0.34830152 -0.234153433 -0.04063432116 -2.837675606
5 v4 -0.84568695 -0.136963126 -0.13084281035 -1.680828329
6 v4 -0.32933343 -0.157789620 -0.02997847693 -0.947623773
7 v4 0.35984044 -0.157789620 0.12732080268 -0.947623773
8 v4 -0.32511830 -0.023574435 -0.10281705810 -2.607366431
9 v4 1.51478626 0.001880170 0.08155320398 -0.637055341
10 v4 0.11114773 -0.224897213 -0.17932134171 -1.818396455
11 v4 0.27975296 -0.109194467 -0.14338902206 2.170944974
12 v4 -0.89626852 -0.069855533 -0.02058415581 -0.658126752
13 v4 0.12379312 -0.123078796 -0.11528274705 -0.808243774
14 v4 0.66965255 -0.111508522 -0.11764091337 2.377766908
15 v4 1.56536783 -0.143905291 0.04389156236 2.111220276
16 v4 0.56427428 -0.099938247 0.01399844913 -0.322326312
17 v4 -0.71291033 -0.118450687 -0.05755560242 2.218858946
18 v4 -0.75927677 1.519900201 0.04711630687 3.920878638
19 v4 -0.75295407 0.177748344 0.01584280360 -0.304945754
20 v4 -1.00164679 0.108326696 0.09348590900 1.038591535
21 v4 -1.03958296 0.652129604 0.09677967302 1.752268128
22 v4 0.82139726 0.638245274 0.02053612974 0.907465624
23 v4 -1.07541157 -0.072169588 -0.03608286844 1.137774798
24 v4 -1.03115270 0.087500202 0.07805238146 -3.663486997
25 v4 -0.98900139 -0.180930170 -0.00009686695 2.350924346
26 v4 -1.06908888 -0.146219346 -0.02285413055 0.067293462
27 v4 -1.20186549 -0.049029039 -0.00424187149 -1.898454393
28 v4 0.58324237 -0.125392851 0.01446241356 -2.497647463
29 v4 -0.97003330 -0.134649071 0.03187450017 -4.471716512
30 v4 0.22917139 -0.060599313 0.11323315542 -1.465081244
31 G8 0.41042201 -0.086053918 -0.01171898422 -0.232806371
32 G8 -1.11545531 -0.197128554 -0.06499053655 -3.043893581
33 G8 -0.19023412 -0.083739863 -0.07758659568 -2.323908986
34 G8 0.25446217 -0.092996082 -0.07399758157 1.437404886
35 G8 -0.05324237 0.844196163 -0.11503350996 1.079056696
36 G8 0.09007207 0.055103433 0.02167111711 1.110865131
37 G8 1.21129685 1.971140911 0.01904454162 1.404724068
38 G8 0.62539368 -0.111508522 0.05768779393 -1.706664294
39 G8 1.32932051 -0.224897213 0.05555202379 0.736746935
40 G8 0.40199175 -0.187872334 -0.01031175326 -0.005516985
41 G8 0.44625062 -0.160103675 -0.00458313459 1.727170333
42 G8 0.60221046 -0.194814499 0.17430774591 1.685228831
43 G8 0.33665722 -0.053657149 0.00481502094 1.836016918
44 G8 -0.63493041 -0.206384774 -0.00928412956 0.466173920
45 G8 -0.28296700 0.108326696 0.09047589183 1.697173771
46 G8 -0.32722587 -0.164731785 0.08917985896 1.057314221
47 G8 -0.11646933 0.187004564 -0.05671203072 0.933704227
48 G8 -0.10171637 0.025020719 -0.05333390954 0.482480775
49 G8 0.13643851 0.057417488 0.08541446168 0.680713089
50 G8 -0.57802615 0.434608441 0.10140397965 0.090780703
51 G8 0.05002833 0.057417488 -0.02509342995 0.680713089
52 G8 -0.16072820 0.073615872 -0.03698779080 -0.982921741
53 G8 -0.29139726 -0.035144709 0.04609635201 -2.281900378
54 G8 0.13222338 -0.051343094 0.06524159499 0.972089090
55 G8 -0.41152848 -0.134649071 0.08459773090 0.027767791
56 G8 0.68229794 -0.185558279 -0.03239032508 -0.162881500
57 G8 -0.24292325 0.013450444 -0.03208740616 -0.530221948
58 G8 -0.11646933 -0.134649071 0.06264952925 -0.385741863
59 G8 -0.21341734 -0.215640993 0.05241547086 -0.972251823
60 G8 -0.24292325 -0.185558279 -0.03437271856 0.002267358
61 G8 -0.24292325 -0.005061995 -0.03437271856 -1.134447998
62 G8 0.09007207 -0.238781543 -0.06747523863 0.626424009
63 G8 -0.34197883 -0.099938247 -0.01270059491 -0.722750217
64 G8 -0.30825778 -0.167045840 0.10014629095 -0.382722075
65 G8 -0.08696342 -0.208698829 -0.02872845706 -0.356550578
66 G8 -0.81196590 0.048161268 -0.00950652573 -1.851614124
67 G8 0.49683219 0.048161268 0.04867308008 -1.851614124
68 G8 -0.13754498 -0.037458764 0.02486518629 1.731465143
69 G8 -0.48318570 0.161549960 -0.05951115497 0.254319006
70 G8 0.39988418 0.031962884 -0.02353665674 2.043778341
71 G8 0.90148474 -0.102252302 -0.01967923345 -0.289913920
72 G8 0.28396809 -0.123078796 -0.10148651548 1.386940871
73 G8 1.05322945 -0.139277181 -0.00480936518 0.054207713
74 G8 1.24923303 -0.208698829 -0.00098261723 0.594212936
75 G8 0.47154141 -0.118450687 -0.13970798195 1.551821303
76 G8 1.27873894 -0.072169588 -0.00286148145 3.100704184
77 G8 0.05002833 -0.044400929 -0.05492902692 0.327263666
78 G8 1.54218461 -0.030516599 0.10732815358 -1.055195336
79 G8 0.74763247 -0.132335016 0.11660744219 -1.134447998
80 G8 0.11747042 -0.037458764 -0.02016620439 1.730726972
Family Hunting Not.Hunting Grooming Not Grooming
1 v4 -0.67708172 0.114961983 0.2644238 0.105443109
2 v4 -0.58224128 0.556326739 -1.9467488 -0.249016133
3 v4 -0.11436177 0.326951992 2.1597867 -0.563247851
4 v4 -0.34830152 0.795734469 2.1698228 -0.611969290
5 v4 -0.84568695 0.770046573 0.2554708 -0.230476117
6 v4 -0.32933343 0.736574466 0.1225477 -0.270401826
7 v4 0.35984044 0.215724268 0.1225477 1.057451389
8 v4 -0.32511830 -0.200731013 0.2593696 -0.260830004
9 v4 1.51478626 -2.160535836 0.8687508 1.030589923
10 v4 0.11114773 0.660462182 1.7955299 -0.809959417
11 v4 0.27975296 -0.293709087 -0.8377330 -0.292132450
12 v4 -0.89626852 0.565754284 1.3339454 -0.573854465
13 v4 0.12379312 -0.499644710 -0.5100101 -0.372285683
14 v4 0.66965255 0.080624964 -2.6852985 -0.470590886
15 v4 1.56536783 -4.076143639 -0.8432925 1.657328707
16 v4 0.56427428 -0.127040484 -0.8662526 -0.161145079
17 v4 -0.71291033 0.661240603 -2.1990933 -0.381900622
18 v4 -0.75927677 0.294950237 -3.5062302 -0.121909231
19 v4 -0.75295407 0.548369546 -1.3326746 -0.338568723
20 v4 -1.00164679 0.137622686 -1.7580862 -0.312742050
21 v4 -1.03958296 0.019302681 -2.2730277 0.708985315
22 v4 0.82139726 -0.043057497 -3.1829838 -0.378408200
23 v4 -1.07541157 0.351515502 -0.3762928 -0.304161903
24 v4 -1.03115270 -0.007163636 1.3605877 -0.431053223
25 v4 -0.98900139 0.253780410 -1.1388134 -0.554883286
26 v4 -1.06908888 0.700680605 0.6629041 0.113074697
27 v4 -1.20186549 0.340704098 0.9979915 -0.693545361
28 v4 0.58324237 -1.727041782 1.5589254 0.180163686
29 v4 -0.97003330 0.209410408 1.7613786 -0.258156792
30 v4 0.22917139 -2.441026901 1.3929340 0.276959818
31 G8 0.41042201 0.383257784 -0.5374467 0.165978418
32 G8 -1.11545531 -1.098682982 2.9654839 0.148947473
33 G8 -0.19023412 0.873144122 2.5120581 -0.846910101
34 G8 0.25446217 0.968889915 -0.4130434 -0.938661624
35 G8 -0.05324237 0.936455703 -2.5993065 -0.949914982
36 G8 0.09007207 -0.467815937 -1.0766479 1.474170593
37 G8 1.21129685 -1.239490708 -4.1335895 1.357023559
38 G8 0.62539368 0.177235670 2.4989896 1.393241265
39 G8 1.32932051 -4.736158229 -0.5718146 2.467225606
40 G8 0.40199175 0.342693397 0.5675981 0.648320657
41 G8 0.44625062 0.488950070 -1.6998195 0.709588943
42 G8 0.60221046 -0.415575233 -1.4313741 0.728473890
43 G8 0.33665722 0.353937257 -2.2985148 0.379706002
44 G8 -0.63493041 0.262083568 0.2245685 -0.367629121
45 G8 -0.28296700 0.574316915 -1.0020637 0.280710938
46 G8 -0.32722587 0.323665326 -1.1559252 0.119455912
47 G8 -0.11646933 0.786566398 0.1746772 -0.858206576
48 G8 -0.10171637 0.718065343 -0.2673407 -0.552555005
49 G8 0.13643851 0.584868846 -0.1203383 -0.335378116
50 G8 -0.57802615 -0.053955393 0.6359729 0.057885811
51 G8 0.05002833 0.738563765 -0.1203383 -0.188308359
52 G8 -0.16072820 0.778263240 2.1906890 -0.545138998
53 G8 -0.29139726 0.751018502 1.6039070 0.198100074
54 G8 0.13222338 0.297804447 -0.5217068 -0.514310832
55 G8 -0.41152848 0.102161281 0.3866610 -0.036323341
56 G8 0.68229794 0.371667959 1.6179863 -0.176365139
57 G8 -0.24292325 0.631574111 1.4206594 -0.269668849
58 G8 -0.11646933 -0.004568899 1.6827511 0.003731717
59 G8 -0.21341734 0.214080935 1.0590019 0.036586351
60 G8 -0.24292325 0.796339908 1.2727184 -0.615289246
61 G8 -0.24292325 0.796339908 2.6745838 -0.615289246
62 G8 0.09007207 -0.396720145 0.2644238 0.290800156
63 G8 -0.34197883 0.441985331 1.4545220 -0.520648930
64 G8 -0.30825778 -2.489721464 1.3587105 1.711267220
65 G8 -0.08696342 0.407907785 0.8136610 -0.273333736
66 G8 -0.81196590 0.554423932 1.3666527 -0.594420949
67 G8 0.49683219 0.697912886 1.3666527 -0.446661330
68 G8 -0.13754498 0.491198842 -1.3307974 -0.333825929
69 G8 -0.48318570 0.604848320 -0.1305910 -0.601492025
70 G8 0.39988418 0.773938679 -0.5078441 -0.712559657
71 G8 0.90148474 0.734412186 -0.1166561 -0.548803885
72 G8 0.28396809 1.145505011 -1.3062489 -0.921846260
73 G8 1.05322945 0.616784110 0.9039851 -0.165629176
74 G8 1.24923303 0.329287256 0.3647117 0.111867440
75 G8 0.47154141 -0.016764163 -1.1586689 -0.476713403
76 G8 1.27873894 0.007799347 -3.0386529 0.215087903
77 G8 0.05002833 0.209496900 -1.5080522 0.324560232
78 G8 1.54218461 -5.031179821 1.6811626 2.366893936
79 G8 0.74763247 -0.325105405 1.6851337 1.351590903
80 G8 0.11747042 -0.756350687 -1.3315194 0.375911766
Family Other
1 v4 0.019502286
2 v4 -0.290451956
3 v4 0.359948884
4 v4 0.557840914
5 v4 0.117453376
6 v4 0.126645924
7 v4 0.126645924
8 v4 0.196486873
9 v4 0.152780228
10 v4 0.354469789
11 v4 -0.261430968
12 v4 0.176448238
13 v4 -0.007374708
14 v4 -0.557848621
15 v4 -0.213674557
16 v4 -0.005819262
17 v4 -0.470070992
18 v4 -0.786078864
19 v4 0.006063789
20 v4 -0.271842650
21 v4 -0.349418792
22 v4 -0.338096262
23 v4 -0.165119403
24 v4 0.346566439
25 v4 -0.344191931
26 v4 0.074321265
27 v4 0.179825379
28 v4 0.278407054
29 v4 0.593125727
30 v4 0.199177375
31 G8 -0.058900625
32 G8 0.633875622
33 G8 0.428150308
34 G8 -0.206023441
35 G8 -0.436958199
36 G8 -0.291839246
37 G8 -0.907641911
38 G8 0.448567295
39 G8 -0.127186127
40 G8 0.024715134
41 G8 -0.416345030
42 G8 -0.330697382
43 G8 -0.469720666
44 G8 -0.047494017
45 G8 -0.301732446
46 G8 -0.138901021
47 G8 0.098101379
48 G8 -0.002063769
49 G8 -0.028324190
50 G8 0.071630763
51 G8 -0.028324190
52 G8 0.295110588
53 G8 0.347112947
54 G8 -0.083577573
55 G8 -0.036886152
56 G8 0.189045953
57 G8 0.467596992
58 G8 0.303378276
59 G8 0.218879697
60 G8 0.092005711
61 G8 0.270111340
62 G8 -0.012909856
63 G8 0.262292068
64 G8 0.107125772
65 G8 0.123422927
66 G8 0.299426602
67 G8 0.299426602
68 G8 -0.326871824
69 G8 -0.022088391
70 G8 -0.428508341
71 G8 -0.014675497
72 G8 -0.114462294
73 G8 0.087227267
74 G8 -0.031519161
75 G8 -0.159318008
76 G8 -0.397875854
77 G8 0.101520559
78 G8 0.244481505
79 G8 0.529968994
80 G8 -0.326619590
推荐答案
<我想我拼凑了一些问题。此响应部分有助于显示如何创建可重现的示例（基于已提供的建议）并在回答问题上取得一些进展
I think I pieced together some of the problem. This response is partly to help show how to create a reproducible example (based on suggestions already provided) and to make some progress in answering the question
步骤1：提供数据
下面直接是 dput（mydat）生成的输出。可以将整个块复制到R中，并立即看到 mydat 数据框，而不必尝试进行大量手动加载和编辑。
Directly below is the output generated from dput(mydat). One can copy this entire block into R and instantly see the mydat data frame, rather than try to finagle with a bunch of manual loading and editing.
mydat <- structure(list(Family = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("G8", "v4"), class = "factor"),
Swimming = c(-0.4805568, 0.12600625, 0.06823834, 0.67480139,
0.64591744, 0.21265812, -0.01841352, 0.12600625, -0.2206012,
0.27042603, 0.03935439, -0.45167284, -0.04729748, -0.10506539,
0.0971223, -0.07618143, 0.29930998, 0.01047043, -0.24948516,
-0.04729748, -0.01841352, -0.19171725, -0.4805568, 0.01047043,
-0.42278889, -0.45167284, -0.30725307, 0.24154207, 1.45466817,
-0.01841352, 0.38596185, 0.15489021, -0.04729748, 0.27042603,
-0.07618143, -0.10506539, -0.01841352, 0.01047043, 0.06823834,
-0.16283329, -0.01841352, -0.39390493, -0.04729748, 0.01047043,
0.01047043, 0.06823834, -0.04729748, -0.2206012, -0.16283329,
-0.07618143, -0.2206012, -0.19171725, -0.16283329, -0.2206012,
-0.13394934, -0.27836911, -0.04729748, 0.01047043, 0.12600625,
0.06823834, 0.06823834, 0.32819394, 0.32819394, -0.27836911,
0.18377416, 0.55926557, -0.19171725, -0.19171725, 0.01047043,
-0.19171725, -0.01841352, -0.07618143, -0.13394934, -0.39390493,
-0.04729748, -0.27836911, 0.70368535, 0.29930998, -0.13394934,
0.21265812), Not.Swimming = c(-0.0862927, -0.074481895, -0.056765686,
-0.050860283, -0.050860283, -0.068576492, -0.068576492, 0.05543697,
0.114491, -0.021333268, -0.04495488, 0.008193747, -0.056765686,
0.008193747, 0.037720761, 0.01409915, 0.108585597, -0.074481895,
0.002288344, 0.049531567, 0.043626164, 0.049531567, 0.020004552,
0.008193747, 0.025909955, 0.031815358, 0.049531567, -0.039049477,
-0.003617059, 0.002288344, 0.084963985, -0.080387298, 0.067247776,
0.031815358, 0.037720761, 0.025909955, 0.126301805, 0.031815358,
0.037720761, -0.050860283, -0.039049477, -0.003617059, 0.008193747,
-0.039049477, -0.003617059, 0.008193747, 0.01409915, -0.015427865,
0.020004552, 0.031815358, 0.020004552, -0.033144074, -0.039049477,
-0.009522462, -0.003617059, -0.04495488, -0.050860283, -0.04495488,
-0.068576492, -0.033144074, -0.027238671, -0.068576492, 0.01409915,
0.002288344, 0.025909955, -0.009522462, -0.009522462, 0.025909955,
0.15582882, 0.002288344, -0.04495488, -0.015427865, 0.008193747,
0.037720761, 0.008193747, -0.015427865, -0.056765686, 0.079058582,
-0.056765686, 0.025909955), Running = c(-0.157157188, 0.057316151,
0.064711783, 0.153459372, 0.072107416, 0.057316151, -0.053618335,
0.012942357, -0.03882707, 0.049920519, 0.012942357, -0.075805232,
0.035129254, -0.046222702, 0.109085578, -0.03882707, 0.057316151,
0.020337989, 0.035129254, 0.057316151, 0.005546724, -0.016640173,
-0.142365923, 0.220020063, -0.149761556, -0.134970291, 0.042524886,
0.072107416, 0.064711783, 0.020337989, 0.049920519, 0.020337989,
0.138668107, 0.049920519, 0.020337989, -0.083200864, -0.024035805,
-0.016640173, -0.03882707, -0.03882707, 0.005546724, -0.090596497,
-0.00924454, -0.016640173, -0.075805232, -0.090596497, 0.012942357,
-0.075805232, -0.061013967, -0.03882707, -0.112783394, -0.068409599,
-0.090596497, -0.053618335, -0.075805232, -0.090596497, 0.064711783,
0.012942357, 0.042524886, -0.061013967, -0.061013967, 0.064711783,
0.175646269, -0.068409599, 0.027733621, 0.042524886, -0.03882707,
-0.00924454, 0.027733621, -0.031431438, -0.046222702, -0.031431438,
-0.068409599, -0.120179026, 0.035129254, -0.061013967, 0.39751524,
0.138668107, 0.020337989, 0.035129254), Not.Running = c(-0.438809944,
-0.539013927, -0.539013927, -0.539013927, -0.472211271, -0.071395338,
-0.071395338, 0.296019267, 0.563229889, -0.03799401, 0.195815284,
-0.171599321, -0.305204632, 0.062209973, -0.104796666, 0.095611301,
0.028808645, -0.071395338, 0.329420595, 0.296019267, -0.171599321,
-0.071395338, 0.596631217, 0.062209973, 0.028808645, -0.138197994,
0.095611301, -0.104796666, 0.296019267, 0.028808645, -0.03799401,
-0.33860596, 0.129012629, 0.195815284, -0.03799401, 0.396223251,
0.362821923, -0.138197994, 0.26261794, -0.405408616, -0.205000649,
0.129012629, 0.195815284, -0.205000649, -0.004592683, -0.205000649,
-0.071395338, -0.171599321, -0.104796666, -0.138197994, -0.104796666,
-0.071395338, -0.104796666, -0.03799401, -0.004592683, -0.238401977,
0.028808645, -0.305204632, -0.305204632, -0.271803305, -0.03799401,
-0.372007288, 0.095611301, 0.195815284, 0.162413956, 0.229216612,
0.229216612, 0.396223251, 0.630032545, 0.463025906, 0.496427234,
0.062209973, -0.071395338, 0.229216612, -0.071395338, -0.071395338,
-0.205000649, 0.229216612, -0.305204632, 0.396223251), Fighting = c(-0.67708172,
-0.58224128, -0.11436177, -0.34830152, -0.84568695, -0.32933343,
0.35984044, -0.3251183, 1.51478626, 0.11114773, 0.27975296,
-0.89626852, 0.12379312, 0.66965255, 1.56536783, 0.56427428,
-0.71291033, -0.75927677, -0.75295407, -1.00164679, -1.03958296,
0.82139726, -1.07541157, -1.0311527, -0.98900139, -1.06908888,
-1.20186549, 0.58324237, -0.9700333, 0.22917139, 0.41042201,
-1.11545531, -0.19023412, 0.25446217, -0.05324237, 0.09007207,
1.21129685, 0.62539368, 1.32932051, 0.40199175, 0.44625062,
0.60221046, 0.33665722, -0.63493041, -0.282967, -0.32722587,
-0.11646933, -0.10171637, 0.13643851, -0.57802615, 0.05002833,
-0.1607282, -0.29139726, 0.13222338, -0.41152848, 0.68229794,
-0.24292325, -0.11646933, -0.21341734, -0.24292325, -0.24292325,
0.09007207, -0.34197883, -0.30825778, -0.08696342, -0.8119659,
0.49683219, -0.13754498, -0.4831857, 0.39988418, 0.90148474,
0.28396809, 1.05322945, 1.24923303, 0.47154141, 1.27873894,
0.05002833, 1.54218461, 0.74763247, 0.11747042), Not.Fighting = c(-0.097624192,
-0.160103675, -0.092996082, -0.234153433, -0.136963126, -0.15778962,
-0.15778962, -0.023574435, 0.00188017, -0.224897213, -0.109194467,
-0.069855533, -0.123078796, -0.111508522, -0.143905291, -0.099938247,
-0.118450687, 1.519900201, 0.177748344, 0.108326696, 0.652129604,
0.638245274, -0.072169588, 0.087500202, -0.18093017, -0.146219346,
-0.049029039, -0.125392851, -0.134649071, -0.060599313, -0.086053918,
-0.197128554, -0.083739863, -0.092996082, 0.844196163, 0.055103433,
1.971140911, -0.111508522, -0.224897213, -0.187872334, -0.160103675,
-0.194814499, -0.053657149, -0.206384774, 0.108326696, -0.164731785,
0.187004564, 0.025020719, 0.057417488, 0.434608441, 0.057417488,
0.073615872, -0.035144709, -0.051343094, -0.134649071, -0.185558279,
0.013450444, -0.134649071, -0.215640993, -0.185558279, -0.005061995,
-0.238781543, -0.099938247, -0.16704584, -0.208698829, 0.048161268,
0.048161268, -0.037458764, 0.16154996, 0.031962884, -0.102252302,
-0.123078796, -0.139277181, -0.208698829, -0.118450687, -0.072169588,
-0.044400929, -0.030516599, -0.132335016, -0.037458764),
Resting = c(0.01081204879, -0.03398160805, 0.057108797, -0.04063432116,
-0.13084281035, -0.02997847693, 0.12732080268, -0.1028170581,
0.08155320398, -0.17932134171, -0.14338902206, -0.02058415581,
-0.11528274705, -0.11764091337, 0.04389156236, 0.01399844913,
-0.05755560242, 0.04711630687, 0.0158428036, 0.093485909,
0.09677967302, 0.02053612974, -0.03608286844, 0.07805238146,
-9.686695e-05, -0.02285413055, -0.00424187149, 0.01446241356,
0.03187450017, 0.11323315542, -0.01171898422, -0.06499053655,
-0.07758659568, -0.07399758157, -0.11503350996, 0.02167111711,
0.01904454162, 0.05768779393, 0.05555202379, -0.01031175326,
-0.00458313459, 0.17430774591, 0.00481502094, -0.00928412956,
0.09047589183, 0.08917985896, -0.05671203072, -0.05333390954,
0.08541446168, 0.10140397965, -0.02509342995, -0.0369877908,
0.04609635201, 0.06524159499, 0.0845977309, -0.03239032508,
-0.03208740616, 0.06264952925, 0.05241547086, -0.03437271856,
-0.03437271856, -0.06747523863, -0.01270059491, 0.10014629095,
-0.02872845706, -0.00950652573, 0.04867308008, 0.02486518629,
-0.05951115497, -0.02353665674, -0.01967923345, -0.10148651548,
-0.00480936518, -0.00098261723, -0.13970798195, -0.00286148145,
-0.05492902692, 0.10732815358, 0.11660744219, -0.02016620439
), Not.Resting = c(-0.77046287, 0.773856776, -2.593072768,
-2.837675606, -1.680828329, -0.947623773, -0.947623773, -2.607366431,
-0.637055341, -1.818396455, 2.170944974, -0.658126752, -0.808243774,
2.377766908, 2.111220276, -0.322326312, 2.218858946, 3.920878638,
-0.304945754, 1.038591535, 1.752268128, 0.907465624, 1.137774798,
-3.663486997, 2.350924346, 0.067293462, -1.898454393, -2.497647463,
-4.471716512, -1.465081244, -0.232806371, -3.043893581, -2.323908986,
1.437404886, 1.079056696, 1.110865131, 1.404724068, -1.706664294,
0.736746935, -0.005516985, 1.727170333, 1.685228831, 1.836016918,
0.46617392, 1.697173771, 1.057314221, 0.933704227, 0.482480775,
0.680713089, 0.090780703, 0.680713089, -0.982921741, -2.281900378,
0.97208909, 0.027767791, -0.1628815, -0.530221948, -0.385741863,
-0.972251823, 0.002267358, -1.134447998, 0.626424009, -0.722750217,
-0.382722075, -0.356550578, -1.851614124, -1.851614124, 1.731465143,
0.254319006, 2.043778341, -0.28991392, 1.386940871, 0.054207713,
0.594212936, 1.551821303, 3.100704184, 0.327263666, -1.055195336,
-1.134447998, 1.730726972), Hunting = c(-0.67708172, -0.58224128,
-0.11436177, -0.34830152, -0.84568695, -0.32933343, 0.35984044,
-0.3251183, 1.51478626, 0.11114773, 0.27975296, -0.89626852,
0.12379312, 0.66965255, 1.56536783, 0.56427428, -0.71291033,
-0.75927677, -0.75295407, -1.00164679, -1.03958296, 0.82139726,
-1.07541157, -1.0311527, -0.98900139, -1.06908888, -1.20186549,
0.58324237, -0.9700333, 0.22917139, 0.41042201, -1.11545531,
-0.19023412, 0.25446217, -0.05324237, 0.09007207, 1.21129685,
0.62539368, 1.32932051, 0.40199175, 0.44625062, 0.60221046,
0.33665722, -0.63493041, -0.282967, -0.32722587, -0.11646933,
-0.10171637, 0.13643851, -0.57802615, 0.05002833, -0.1607282,
-0.29139726, 0.13222338, -0.41152848, 0.68229794, -0.24292325,
-0.11646933, -0.21341734, -0.24292325, -0.24292325, 0.09007207,
-0.34197883, -0.30825778, -0.08696342, -0.8119659, 0.49683219,
-0.13754498, -0.4831857, 0.39988418, 0.90148474, 0.28396809,
1.05322945, 1.24923303, 0.47154141, 1.27873894, 0.05002833,
1.54218461, 0.74763247, 0.11747042), Not.Hunting = c(-0.097624192,
-0.160103675, -0.092996082, -0.234153433, -0.136963126, -0.15778962,
-0.15778962, -0.023574435, 0.00188017, -0.224897213, -0.109194467,
-0.069855533, -0.123078796, -0.111508522, -0.143905291, -0.099938247,
-0.118450687, 1.519900201, 0.177748344, 0.108326696, 0.652129604,
0.638245274, -0.072169588, 0.087500202, -0.18093017, -0.146219346,
-0.049029039, -0.125392851, -0.134649071, -0.060599313, -0.086053918,
-0.197128554, -0.083739863, -0.092996082, 0.844196163, 0.055103433,
1.971140911, -0.111508522, -0.224897213, -0.187872334, -0.160103675,
-0.194814499, -0.053657149, -0.206384774, 0.108326696, -0.164731785,
0.187004564, 0.025020719, 0.057417488, 0.434608441, 0.057417488,
0.073615872, -0.035144709, -0.051343094, -0.134649071, -0.185558279,
0.013450444, -0.134649071, -0.215640993, -0.185558279, -0.005061995,
-0.238781543, -0.099938247, -0.16704584, -0.208698829, 0.048161268,
0.048161268, -0.037458764, 0.16154996, 0.031962884, -0.102252302,
-0.123078796, -0.139277181, -0.208698829, -0.118450687, -0.072169588,
-0.044400929, -0.030516599, -0.132335016, -0.037458764),
Grooming = c(0.01081204879, -0.03398160805, 0.057108797,
-0.04063432116, -0.13084281035, -0.02997847693, 0.12732080268,
-0.1028170581, 0.08155320398, -0.17932134171, -0.14338902206,
-0.02058415581, -0.11528274705, -0.11764091337, 0.04389156236,
0.01399844913, -0.05755560242, 0.04711630687, 0.0158428036,
0.093485909, 0.09677967302, 0.02053612974, -0.03608286844,
0.07805238146, -9.686695e-05, -0.02285413055, -0.00424187149,
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0.101520559, 0.244481505, 0.529968994, -0.32661959)), .Names = c("Family",
"Swimming", "Not.Swimming", "Running", "Not.Running", "Fighting",
"Not.Fighting", "Resting", "Not.Resting", "Hunting", "Not.Hunting",
"Grooming", "Not.Grooming", "Other"), class = "data.frame", row.names = c(NA,
-80L))
Note that this object is very large. You will generally get more (and better) responses if you provide minimal code. This isn’t always possible, but in my experience most people don’t want to copy/paste very large objects.
Note that this object is very large. You will generally get more (and better) responses if you provide minimal code. This isn't always possible, but in my experience most people don't want to copy/paste very large objects.
Step 2: After providing the data, supply all of the code needed, in sequence, before the function that generates the warning/error/message
Step 2: After providing the data, supply all of the code needed, in sequence, before the function that generates the warning/error/message
Your post linked to another post that defined mydat_resampled. Don’t force posters to manually piece this information together from multiple posts - just provide all of the code needed in one post.
Your post linked to another post that defined mydat_resampled. Don't force posters to manually piece this information together from multiple posts - just provide all of the code needed in one post.
# Randomly permute the data before subsetting
set.seed(1234)
mydat_idx <- sample(1:nrow(mydat), replace = FALSE)
mydat <- mydat[mydat_idx, ]
require(caret)
mydat_resampled_idx <- createDataPartition(mydat_idx, times = 1, p = 0.7, list = FALSE)
mydat_resampled <- mydat[mydat_resampled_idx, ] # Training portion of the data
Step 3: Make it clear to readers which line of code is generating the error
These lines do not produce a warning
These lines do not produce a warning
nb_tune <- data.frame(usekernel =TRUE, fL = 0)
set.seed(1234)
nb_mod <- train(x = mydat_resampled[, 2:13], y = as.factor(mydat_resampled[, 1]),
method = "nb", trControl = trainControl(method = "cv", classProbs =
TRUE), tuneGrid = nb_tune)
But changing 2:13 to 2:14 produces a warning
But changing 2:13 to 2:14 produces a warning
set.seed(1234)
nb_mod <- train(x = mydat_resampled[, 2:14], y = as.factor(mydat_resampled[, 1]),
method = "nb", trControl = trainControl(method = "cv", classProbs =
TRUE), tuneGrid = nb_tune)
Warning messages:
1: In FUN(X[[i]], ...) :
Numerical 0 probability for all classes with observation 2
2: In FUN(X[[i]], ...) :
Numerical 0 probability for all classes with observation 2
As far as the answer goes, user20650’s link provides the most likely explanation - that observation 2 is probably an outlier. As a result, the probability that this observation is 0 for both v4 and G8.
As far as the answer goes, user20650's link provides the most likely explanation - that observation 2 is probably an outlier. As a result, the probability that this observation is 0 for both v4 and G8.
This is not an error or indication that the code is ’wrong’, it is just information to let you know that one of your observations is producing some unusual probabilities - something you may want to examine in either your data or modeling approach.
This is not an error or indication that the code is 'wrong', it is just information to let you know that one of your observations is producing some unusual probabilities - something you may want to examine in either your data or modeling approach.
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