I am new to cross-validation and I have a data-set called LDA.scores for 12 measured call-type parameters. I am trying to run a k-fold repeated cross validation with 10 folds and associated naive Bayes method. The grouping factor is Family, since I am trying to assimilate if call-type parameters between between both families are different. I am trying to run this code
library(caret)
train_control<-trainControl(method="repeatedcv", number=10, repeats=3)
model<-train(Family~., data=LDA.scores, trControl=train_control,method="nb")
predictions <- predict(model, LDA.scores[,2:13])
confusionMatrix(predictions,LDA.scores$Family)
I keep on getting these error messages:
Error in train.default(x, y, weights = w, ...) :
wrong model type for regression
I do not understand what I am doing wrong. How can I run this code to produce a naive Bayes matrix. Any advice would be deeply appreciated. I have tried everything possible with my novel capabilities. Words cannot describe my gratitude if anyone has a solution. Here is a portion of my dataframe:
Family SBI.max.Part.1 SBI.max.Part.2 SBI.min.Part.1 SBI.min.Part.2
1 G8 -0.48055680 -0.086292700 -0.157157188 -0.438809944
2 G8 0.12600625 -0.074481895 0.057316151 -0.539013927
3 G8 0.06823834 -0.056765686 0.064711783 -0.539013927
4 G8 0.67480139 -0.050860283 0.153459372 -0.539013927
5 G8 0.64591744 -0.050860283 0.072107416 -0.472211271
6 G8 0.21265812 -0.068576492 0.057316151 -0.071395338
7 G8 -0.01841352 -0.068576492 -0.053618335 -0.071395338
8 G8 0.12600625 0.055436970 0.012942357 0.296019267
9 G8 -0.22060120 0.114491000 -0.038827070 0.563229889
10 G8 0.27042603 -0.021333268 0.049920519 -0.037994010
11 G8 0.03935439 -0.044954880 0.012942357 0.195815284
12 G8 -0.45167284 0.008193747 -0.075805232 -0.171599321
13 G8 -0.04729748 -0.056765686 0.035129254 -0.305204632
14 G8 -0.10506539 0.008193747 -0.046222702 0.062209973
15 G8 0.09712230 0.037720761 0.109085578 -0.104796666
16 G8 -0.07618143 0.014099150 -0.038827070 0.095611301
17 G8 0.29930998 0.108585597 0.057316151 0.028808645
18 G8 0.01047043 -0.074481895 0.020337989 -0.071395338
19 G8 -0.24948516 0.002288344 0.035129254 0.329420595
20 G8 -0.04729748 0.049531567 0.057316151 0.296019267
21 G8 -0.01841352 0.043626164 0.005546724 -0.171599321
22 G8 -0.19171725 0.049531567 -0.016640173 -0.071395338
23 G8 -0.48055680 0.020004552 -0.142365923 0.596631217
24 G8 0.01047043 0.008193747 0.220020063 0.062209973
25 G8 -0.42278889 0.025909955 -0.149761556 0.028808645
26 G8 -0.45167284 0.031815358 -0.134970291 -0.138197994
27 G8 -0.30725307 0.049531567 0.042524886 0.095611301
28 G8 0.24154207 -0.039049477 0.072107416 -0.104796666
29 G8 1.45466817 -0.003617059 0.064711783 0.296019267
30 G8 -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 V4 0.27042603 0.031815358 0.049920519 0.195815284
35 V4 -0.07618143 0.037720761 0.020337989 -0.037994010
36 V4 -0.10506539 0.025909955 -0.083200864 0.396223251
37 V4 -0.01841352 0.126301805 -0.024035805 0.362821923
38 V4 0.01047043 0.031815358 -0.016640173 -0.138197994
39 V4 0.06823834 0.037720761 -0.038827070 0.262617940
40 V4 -0.16283329 -0.050860283 -0.038827070 -0.405408616
41 V4 -0.01841352 -0.039049477 0.005546724 -0.205000649
42 V4 -0.39390493 -0.003617059 -0.090596497 0.129012629
43 V4 -0.04729748 0.008193747 -0.009244540 0.195815284
44 V4 0.01047043 -0.039049477 -0.016640173 -0.205000649
45 V4 0.01047043 -0.003617059 -0.075805232 -0.004592683
46 V4 0.06823834 0.008193747 -0.090596497 -0.205000649
47 V4 -0.04729748 0.014099150 0.012942357 -0.071395338
48 V4 -0.22060120 -0.015427865 -0.075805232 -0.171599321
49 V4 -0.16283329 0.020004552 -0.061013967 -0.104796666
50 V4 -0.07618143 0.031815358 -0.038827070 -0.138197994
51 V4 -0.22060120 0.020004552 -0.112783394 -0.104796666
52 V4 -0.19171725 -0.033144074 -0.068409599 -0.071395338
53 V4 -0.16283329 -0.039049477 -0.090596497 -0.104796666
54 V4 -0.22060120 -0.009522462 -0.053618335 -0.037994010
55 V4 -0.13394934 -0.003617059 -0.075805232 -0.004592683
56 V4 -0.27836911 -0.044954880 -0.090596497 -0.238401977
57 V4 -0.04729748 -0.050860283 0.064711783 0.028808645
58 V4 0.01047043 -0.044954880 0.012942357 -0.305204632
59 V4 0.12600625 -0.068576492 0.042524886 -0.305204632
60 V4 0.06823834 -0.033144074 -0.061013967 -0.271803305
61 V4 0.06823834 -0.027238671 -0.061013967 -0.037994010
62 V4 0.32819394 -0.068576492 0.064711783 -0.372007288
63 V4 0.32819394 0.014099150 0.175646269 0.095611301
64 V4 -0.27836911 0.002288344 -0.068409599 0.195815284
65 V4 0.18377416 0.025909955 0.027733621 0.162413956
66 V4 0.55926557 -0.009522462 0.042524886 0.229216612
67 V4 -0.19171725 -0.009522462 -0.038827070 0.229216612
68 V4 -0.19171725 0.025909955 -0.009244540 0.396223251
69 V4 0.01047043 0.155828820 0.027733621 0.630032545
70 V4 -0.19171725 0.002288344 -0.031431438 0.463025906
71 V4 -0.01841352 -0.044954880 -0.046222702 0.496427234
72 V4 -0.07618143 -0.015427865 -0.031431438 0.062209973
73 V4 -0.13394934 0.008193747 -0.068409599 -0.071395338
74 V4 -0.39390493 0.037720761 -0.120179026 0.229216612
75 V4 -0.04729748 0.008193747 0.035129254 -0.071395338
76 V4 -0.27836911 -0.015427865 -0.061013967 -0.071395338
77 V4 0.70368535 -0.056765686 0.397515240 -0.205000649
78 V4 0.29930998 0.079058582 0.138668107 0.229216612
79 V4 -0.13394934 -0.056765686 0.020337989 -0.305204632
80 V4 0.21265812 0.025909955 0.035129254 0.396223251
'data.frame': 80 obs. of 13 variables:
$ Family : Factor w/ 2 levels "G8","V4": 1 1 1 1 1 1 1 1 1 1 .
$ SBI.max.Part.1 : num -0.4806 0.126 0.0682 0.6748 0.6459 ...
$ SBI.max.Part.2 : num -0.0863 -0.0745 -0.0568 -0.0509 -0.0509 ...
$ SBI.min.Part.1 : num -0.1572 0.0573 0.0647 0.1535 0.0721 ...
$ SBI.min.Part.2 : num -0.439 -0.539 -0.539 -0.539 -0.472 ...
Best Answer
You should check out http://topepo.github.io/caret/Bayesian_Model.html
Right now you have a target variable that is continuous and you are trying to apply a classification algorithm to it. Instead, you should use something like
brnnorbartMachine