nueral network testing using iris data

Author

kirit ved

Published

December 3, 2023

test

setting R environment & loading dataset iris

Code

set.seed(1234)
setwd("d:/met/met_lect7")
library("neuralnet")
library("tidyverse")

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::compute() masks neuralnet::compute()
✖ dplyr::filter()  masks stats::filter()
✖ dplyr::lag()     masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Code

library("janitor")


Attaching package: 'janitor'

The following objects are masked from 'package:stats':

    chisq.test, fisher.test

Code

d=iris |>janitor::clean_names()
head(d)

sepal_length	sepal_width	petal_length	petal_width	species
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa

splitting data set in to training & testing

Code

sr=2/3
tmp=sample(1:nrow(d),nrow(d)*sr)
d1=d[tmp,]
d2=d[-tmp,]
nrow(d1);nrow(d2)

[1] 100

[1] 50

running neural network with neural net R package

Code

nn=neuralnet::neuralnet (species~.,d1,
                         hidden=5,
                         linear.output = F)
nn

$call
neuralnet::neuralnet(formula = species ~ ., data = d1, hidden = 5, 
    linear.output = F)

$response
    setosa versicolor virginica
1     TRUE      FALSE     FALSE
2    FALSE       TRUE     FALSE
3    FALSE      FALSE      TRUE
4    FALSE      FALSE      TRUE
5    FALSE      FALSE      TRUE
6    FALSE      FALSE      TRUE
7    FALSE      FALSE      TRUE
8    FALSE      FALSE      TRUE
9    FALSE       TRUE     FALSE
10   FALSE      FALSE      TRUE
11   FALSE       TRUE     FALSE
12   FALSE       TRUE     FALSE
13   FALSE       TRUE     FALSE
14   FALSE      FALSE      TRUE
15    TRUE      FALSE     FALSE
16   FALSE      FALSE      TRUE
17   FALSE       TRUE     FALSE
18    TRUE      FALSE     FALSE
19   FALSE      FALSE      TRUE
20    TRUE      FALSE     FALSE
21   FALSE       TRUE     FALSE
22   FALSE      FALSE      TRUE
23    TRUE      FALSE     FALSE
24   FALSE       TRUE     FALSE
25   FALSE      FALSE      TRUE
26    TRUE      FALSE     FALSE
27   FALSE      FALSE      TRUE
28   FALSE      FALSE      TRUE
29   FALSE       TRUE     FALSE
30    TRUE      FALSE     FALSE
31   FALSE      FALSE      TRUE
32    TRUE      FALSE     FALSE
33   FALSE       TRUE     FALSE
34    TRUE      FALSE     FALSE
35   FALSE      FALSE      TRUE
36   FALSE       TRUE     FALSE
37   FALSE       TRUE     FALSE
38    TRUE      FALSE     FALSE
39    TRUE      FALSE     FALSE
40    TRUE      FALSE     FALSE
41    TRUE      FALSE     FALSE
42   FALSE      FALSE      TRUE
43   FALSE       TRUE     FALSE
44    TRUE      FALSE     FALSE
45   FALSE      FALSE      TRUE
46   FALSE       TRUE     FALSE
47   FALSE       TRUE     FALSE
48    TRUE      FALSE     FALSE
49   FALSE      FALSE      TRUE
50   FALSE      FALSE      TRUE
51   FALSE       TRUE     FALSE
52   FALSE      FALSE      TRUE
53   FALSE       TRUE     FALSE
54    TRUE      FALSE     FALSE
55    TRUE      FALSE     FALSE
56    TRUE      FALSE     FALSE
57   FALSE       TRUE     FALSE
58   FALSE       TRUE     FALSE
59    TRUE      FALSE     FALSE
60    TRUE      FALSE     FALSE
61   FALSE      FALSE      TRUE
62   FALSE      FALSE      TRUE
63   FALSE      FALSE      TRUE
64   FALSE       TRUE     FALSE
65   FALSE      FALSE      TRUE
66    TRUE      FALSE     FALSE
67   FALSE       TRUE     FALSE
68   FALSE       TRUE     FALSE
69    TRUE      FALSE     FALSE
70   FALSE       TRUE     FALSE
71    TRUE      FALSE     FALSE
72   FALSE      FALSE      TRUE
73    TRUE      FALSE     FALSE
74   FALSE      FALSE      TRUE
75   FALSE       TRUE     FALSE
76   FALSE       TRUE     FALSE
77   FALSE       TRUE     FALSE
78   FALSE      FALSE      TRUE
79   FALSE      FALSE      TRUE
80    TRUE      FALSE     FALSE
81   FALSE      FALSE      TRUE
82   FALSE       TRUE     FALSE
83   FALSE       TRUE     FALSE
84    TRUE      FALSE     FALSE
85   FALSE      FALSE      TRUE
86    TRUE      FALSE     FALSE
87    TRUE      FALSE     FALSE
88   FALSE      FALSE      TRUE
89    TRUE      FALSE     FALSE
90   FALSE       TRUE     FALSE
91   FALSE       TRUE     FALSE
92    TRUE      FALSE     FALSE
93   FALSE       TRUE     FALSE
94   FALSE      FALSE      TRUE
95    TRUE      FALSE     FALSE
96   FALSE      FALSE      TRUE
97   FALSE      FALSE      TRUE
98    TRUE      FALSE     FALSE
99   FALSE       TRUE     FALSE
100  FALSE      FALSE      TRUE

$covariate
    sepal_length sepal_width petal_length petal_width
28           5.2         3.5          1.5         0.2
80           5.7         2.6          3.5         1.0
101          6.3         3.3          6.0         2.5
111          6.5         3.2          5.1         2.0
137          6.3         3.4          5.6         2.4
133          6.4         2.8          5.6         2.2
144          6.8         3.2          5.9         2.3
132          7.9         3.8          6.4         2.0
98           6.2         2.9          4.3         1.3
103          7.1         3.0          5.9         2.1
90           5.5         2.5          4.0         1.3
70           5.6         2.5          3.9         1.1
79           6.0         2.9          4.5         1.5
116          6.4         3.2          5.3         2.3
14           4.3         3.0          1.1         0.1
126          7.2         3.2          6.0         1.8
62           5.9         3.0          4.2         1.5
4            4.6         3.1          1.5         0.2
143          5.8         2.7          5.1         1.9
40           5.1         3.4          1.5         0.2
93           5.8         2.6          4.0         1.2
122          5.6         2.8          4.9         2.0
5            5.0         3.6          1.4         0.2
66           6.7         3.1          4.4         1.4
135          6.1         2.6          5.6         1.4
47           5.1         3.8          1.6         0.2
131          7.4         2.8          6.1         1.9
123          7.7         2.8          6.7         2.0
84           6.0         2.7          5.1         1.6
48           4.6         3.2          1.4         0.2
108          7.3         2.9          6.3         1.8
3            4.7         3.2          1.3         0.2
87           6.7         3.1          4.7         1.5
41           5.0         3.5          1.3         0.3
115          5.8         2.8          5.1         2.4
100          5.7         2.8          4.1         1.3
72           6.1         2.8          4.0         1.3
32           5.4         3.4          1.5         0.4
42           4.5         2.3          1.3         0.3
43           4.4         3.2          1.3         0.2
2            4.9         3.0          1.4         0.2
138          6.4         3.1          5.5         1.8
54           5.5         2.3          4.0         1.3
49           5.3         3.7          1.5         0.2
102          5.8         2.7          5.1         1.9
56           5.7         2.8          4.5         1.3
51           7.0         3.2          4.7         1.4
6            5.4         3.9          1.7         0.4
107          4.9         2.5          4.5         1.7
130          7.2         3.0          5.8         1.6
96           5.7         3.0          4.2         1.2
106          7.6         3.0          6.6         2.1
57           6.3         3.3          4.7         1.6
8            5.0         3.4          1.5         0.2
26           5.0         3.0          1.6         0.2
17           5.4         3.9          1.3         0.4
63           6.0         2.2          4.0         1.0
97           5.7         2.9          4.2         1.3
22           5.1         3.7          1.5         0.4
35           4.9         3.1          1.5         0.2
117          6.5         3.0          5.5         1.8
149          6.2         3.4          5.4         2.3
119          7.7         2.6          6.9         2.3
86           6.0         3.4          4.5         1.6
142          6.9         3.1          5.1         2.3
10           4.9         3.1          1.5         0.1
55           6.5         2.8          4.6         1.5
92           6.1         3.0          4.6         1.4
25           4.8         3.4          1.9         0.2
88           6.3         2.3          4.4         1.3
50           5.0         3.3          1.4         0.2
139          6.0         3.0          4.8         1.8
20           5.1         3.8          1.5         0.3
140          6.9         3.1          5.4         2.1
94           5.0         2.3          3.3         1.0
71           5.9         3.2          4.8         1.8
61           5.0         2.0          3.5         1.0
104          6.3         2.9          5.6         1.8
109          6.7         2.5          5.8         1.8
27           5.0         3.4          1.6         0.4
121          6.9         3.2          5.7         2.3
60           5.2         2.7          3.9         1.4
65           5.6         2.9          3.6         1.3
36           5.0         3.2          1.2         0.2
150          5.9         3.0          5.1         1.8
19           5.7         3.8          1.7         0.3
9            4.4         2.9          1.4         0.2
134          6.3         2.8          5.1         1.5
30           4.7         3.2          1.6         0.2
52           6.4         3.2          4.5         1.5
95           5.6         2.7          4.2         1.3
38           4.9         3.6          1.4         0.1
83           5.8         2.7          3.9         1.2
141          6.7         3.1          5.6         2.4
21           5.4         3.4          1.7         0.2
105          6.5         3.0          5.8         2.2
113          6.8         3.0          5.5         2.1
13           4.8         3.0          1.4         0.1
69           6.2         2.2          4.5         1.5
110          7.2         3.6          6.1         2.5

$model.list
$model.list$response
[1] "setosa"     "versicolor" "virginica" 

$model.list$variables
[1] "sepal_length" "sepal_width"  "petal_length" "petal_width" 


$err.fct
function (x, y) 
{
    1/2 * (y - x)^2
}
<bytecode: 0x000001373f5c1b88>
<environment: 0x000001373f5c0500>
attr(,"type")
[1] "sse"

$act.fct
function (x) 
{
    1/(1 + exp(-x))
}
<bytecode: 0x000001373f5bf050>
<environment: 0x000001373f5be758>
attr(,"type")
[1] "logistic"

$linear.output
[1] FALSE

$data
    sepal_length sepal_width petal_length petal_width    species
28           5.2         3.5          1.5         0.2     setosa
80           5.7         2.6          3.5         1.0 versicolor
101          6.3         3.3          6.0         2.5  virginica
111          6.5         3.2          5.1         2.0  virginica
137          6.3         3.4          5.6         2.4  virginica
133          6.4         2.8          5.6         2.2  virginica
144          6.8         3.2          5.9         2.3  virginica
132          7.9         3.8          6.4         2.0  virginica
98           6.2         2.9          4.3         1.3 versicolor
103          7.1         3.0          5.9         2.1  virginica
90           5.5         2.5          4.0         1.3 versicolor
70           5.6         2.5          3.9         1.1 versicolor
79           6.0         2.9          4.5         1.5 versicolor
116          6.4         3.2          5.3         2.3  virginica
14           4.3         3.0          1.1         0.1     setosa
126          7.2         3.2          6.0         1.8  virginica
62           5.9         3.0          4.2         1.5 versicolor
4            4.6         3.1          1.5         0.2     setosa
143          5.8         2.7          5.1         1.9  virginica
40           5.1         3.4          1.5         0.2     setosa
93           5.8         2.6          4.0         1.2 versicolor
122          5.6         2.8          4.9         2.0  virginica
5            5.0         3.6          1.4         0.2     setosa
66           6.7         3.1          4.4         1.4 versicolor
135          6.1         2.6          5.6         1.4  virginica
47           5.1         3.8          1.6         0.2     setosa
131          7.4         2.8          6.1         1.9  virginica
123          7.7         2.8          6.7         2.0  virginica
84           6.0         2.7          5.1         1.6 versicolor
48           4.6         3.2          1.4         0.2     setosa
108          7.3         2.9          6.3         1.8  virginica
3            4.7         3.2          1.3         0.2     setosa
87           6.7         3.1          4.7         1.5 versicolor
41           5.0         3.5          1.3         0.3     setosa
115          5.8         2.8          5.1         2.4  virginica
100          5.7         2.8          4.1         1.3 versicolor
72           6.1         2.8          4.0         1.3 versicolor
32           5.4         3.4          1.5         0.4     setosa
42           4.5         2.3          1.3         0.3     setosa
43           4.4         3.2          1.3         0.2     setosa
2            4.9         3.0          1.4         0.2     setosa
138          6.4         3.1          5.5         1.8  virginica
54           5.5         2.3          4.0         1.3 versicolor
49           5.3         3.7          1.5         0.2     setosa
102          5.8         2.7          5.1         1.9  virginica
56           5.7         2.8          4.5         1.3 versicolor
51           7.0         3.2          4.7         1.4 versicolor
6            5.4         3.9          1.7         0.4     setosa
107          4.9         2.5          4.5         1.7  virginica
130          7.2         3.0          5.8         1.6  virginica
96           5.7         3.0          4.2         1.2 versicolor
106          7.6         3.0          6.6         2.1  virginica
57           6.3         3.3          4.7         1.6 versicolor
8            5.0         3.4          1.5         0.2     setosa
26           5.0         3.0          1.6         0.2     setosa
17           5.4         3.9          1.3         0.4     setosa
63           6.0         2.2          4.0         1.0 versicolor
97           5.7         2.9          4.2         1.3 versicolor
22           5.1         3.7          1.5         0.4     setosa
35           4.9         3.1          1.5         0.2     setosa
117          6.5         3.0          5.5         1.8  virginica
149          6.2         3.4          5.4         2.3  virginica
119          7.7         2.6          6.9         2.3  virginica
86           6.0         3.4          4.5         1.6 versicolor
142          6.9         3.1          5.1         2.3  virginica
10           4.9         3.1          1.5         0.1     setosa
55           6.5         2.8          4.6         1.5 versicolor
92           6.1         3.0          4.6         1.4 versicolor
25           4.8         3.4          1.9         0.2     setosa
88           6.3         2.3          4.4         1.3 versicolor
50           5.0         3.3          1.4         0.2     setosa
139          6.0         3.0          4.8         1.8  virginica
20           5.1         3.8          1.5         0.3     setosa
140          6.9         3.1          5.4         2.1  virginica
94           5.0         2.3          3.3         1.0 versicolor
71           5.9         3.2          4.8         1.8 versicolor
61           5.0         2.0          3.5         1.0 versicolor
104          6.3         2.9          5.6         1.8  virginica
109          6.7         2.5          5.8         1.8  virginica
27           5.0         3.4          1.6         0.4     setosa
121          6.9         3.2          5.7         2.3  virginica
60           5.2         2.7          3.9         1.4 versicolor
65           5.6         2.9          3.6         1.3 versicolor
36           5.0         3.2          1.2         0.2     setosa
150          5.9         3.0          5.1         1.8  virginica
19           5.7         3.8          1.7         0.3     setosa
9            4.4         2.9          1.4         0.2     setosa
134          6.3         2.8          5.1         1.5  virginica
30           4.7         3.2          1.6         0.2     setosa
52           6.4         3.2          4.5         1.5 versicolor
95           5.6         2.7          4.2         1.3 versicolor
38           4.9         3.6          1.4         0.1     setosa
83           5.8         2.7          3.9         1.2 versicolor
141          6.7         3.1          5.6         2.4  virginica
21           5.4         3.4          1.7         0.2     setosa
105          6.5         3.0          5.8         2.2  virginica
113          6.8         3.0          5.5         2.1  virginica
13           4.8         3.0          1.4         0.1     setosa
69           6.2         2.2          4.5         1.5 versicolor
110          7.2         3.6          6.1         2.5  virginica

$exclude
NULL

$net.result
$net.result[[1]]
             [,1]         [,2]          [,3]
28   1.000000e+00 1.411799e-07 2.388316e-194
80   3.003307e-16 9.999994e-01 6.703830e-111
101 2.012853e-174 3.539117e-24  1.000000e+00
111 1.120996e-132 1.158749e-07  1.000000e+00
137 5.250821e-167 4.738235e-20  1.000000e+00
133 1.899538e-168 8.838293e-22  1.000000e+00
144 4.441642e-169 4.206603e-22  1.000000e+00
132 4.547194e-155 6.931091e-17  1.000000e+00
98   6.237746e-60 1.000000e+00  2.019529e-24
103 9.493923e-165 6.628380e-21  1.000000e+00
90   6.273618e-68 1.000000e+00  1.049413e-21
70   1.597293e-54 1.000000e+00  1.214052e-28
79   2.604756e-84 1.000000e+00  7.457735e-14
116 9.027789e-158 6.534106e-17  1.000000e+00
14   1.000000e+00 1.482585e-07 2.260458e-194
126 9.218676e-155 2.751089e-16  1.000000e+00
62   8.090023e-67 1.000000e+00  4.847422e-22
4    1.000000e+00 9.203741e-08 3.861766e-194
143 9.664672e-155 1.442470e-14  1.000000e+00
40   1.000000e+00 1.284651e-07 2.655439e-194
93   1.039818e-56 1.000000e+00  1.114912e-26
122 1.464693e-151 7.875082e-13  1.000000e+00
5    1.000000e+00 1.674748e-07 1.971273e-194
66   5.992588e-56 1.000000e+00  9.225580e-25
135 1.715854e-153 1.467436e-13  1.000000e+00
47   1.000000e+00 1.672694e-07 1.973954e-194
131 1.321405e-164 2.348044e-21  1.000000e+00
123 7.966903e-174 1.810853e-25  1.000000e+00
84  2.128255e-137 5.479361e-08  1.000000e+00
48   1.000000e+00 1.164975e-07 2.963573e-194
108 6.825732e-167 1.233856e-21  1.000000e+00
3    1.000000e+00 1.291223e-07 2.640187e-194
87   1.290336e-73 1.000000e+00  4.201733e-16
41   1.000000e+00 1.489197e-07 2.249306e-194
115 6.242820e-167 1.714343e-20  1.000000e+00
100  5.659775e-62 1.000000e+00  4.246723e-25
72   1.539988e-52 1.000000e+00  3.890269e-28
32   1.000000e+00 9.265676e-08 3.833462e-194
42   1.000000e+00 2.496290e-08 1.672664e-193
43   1.000000e+00 1.294948e-07 2.631486e-194
2    1.000000e+00 9.177118e-08 3.874824e-194
138 3.246673e-149 1.537759e-12  1.000000e+00
54   4.164292e-73 1.000000e+00  2.067070e-18
49   1.000000e+00 1.669564e-07 1.978192e-194
102 9.664672e-155 1.442470e-14  1.000000e+00
56   2.163453e-83 1.000000e+00  4.042428e-16
51   6.049097e-61 1.000000e+00  1.790151e-21
6    1.000000e+00 1.297186e-07 2.626603e-194
107 3.279923e-139 4.387983e-06  9.999989e-01
130 2.276272e-141 7.858541e-12  1.000000e+00
96   1.223162e-57 1.000000e+00  3.590571e-30
106 3.672805e-173 5.980483e-25  1.000000e+00
57   1.028082e-82 1.000000e+00  8.858883e-15
8    1.000000e+00 1.286155e-07 2.651903e-194
26   1.000000e+00 6.930897e-08 5.311365e-194
17   1.000000e+00 1.814336e-07 1.801774e-194
63   1.918420e-54 1.000000e+00  5.936685e-26
97   3.225391e-64 1.000000e+00  1.298914e-24
22   1.000000e+00 1.302283e-07 2.615031e-194
35   1.000000e+00 9.172870e-08 3.876719e-194
117 1.237531e-149 2.523272e-13  1.000000e+00
149 1.796042e-160 7.707361e-17  1.000000e+00
119 3.978950e-177 1.553919e-27  1.000000e+00
86   5.670427e-75 1.000000e+00  8.100283e-20
142 3.643571e-142 3.043777e-13  1.000000e+00
10   1.000000e+00 1.089932e-07 3.193941e-194
55   4.220466e-81 1.000000e+00  2.465796e-12
92   2.911852e-79 1.000000e+00  1.674979e-16
25   1.000000e+00 7.999207e-08 4.520297e-194
88   1.230034e-77 1.000000e+00  5.616052e-13
50   1.000000e+00 1.286436e-07 2.651311e-194
139 1.245089e-120 4.975869e-02  9.663976e-01
20   1.000000e+00 1.617090e-07 2.050393e-194
140 4.695971e-148 1.663705e-14  1.000000e+00
94   1.092502e-44 1.000000e+00  8.072683e-38
71  1.265840e-116 9.551460e-01  3.368214e-02
61   2.437582e-54 1.000000e+00  3.434107e-29
104 1.011038e-158 2.084197e-16  1.000000e+00
109 1.736944e-165 6.634535e-21  1.000000e+00
27   1.000000e+00 8.163407e-08 4.419094e-194
121 7.142859e-164 3.214226e-20  1.000000e+00
60   1.326859e-68 1.000000e+00  2.893884e-23
65   1.105261e-23 9.999999e-01  2.926623e-99
36   1.000000e+00 1.415359e-07 2.381622e-194
150 1.101773e-141 1.012511e-08  1.000000e+00
19   1.000000e+00 1.347508e-07 2.516783e-194
9    1.000000e+00 8.081840e-08 4.468840e-194
134 9.055192e-121 1.085302e-02  9.940260e-01
30   1.000000e+00 9.185905e-08 3.870158e-194
52   6.961618e-67 1.000000e+00  7.041046e-21
95   4.352303e-71 1.000000e+00  5.355405e-21
38   1.000000e+00 1.860237e-07 1.751821e-194
83   7.925301e-52 1.000000e+00  8.506338e-30
141 9.152219e-167 2.096099e-21  1.000000e+00
21   1.000000e+00 1.029348e-07 3.406041e-194
105 1.560606e-169 5.318724e-22  1.000000e+00
113 4.220895e-156 3.036241e-17  1.000000e+00
13   1.000000e+00 1.091695e-07 3.188163e-194
69  3.398108e-102 9.831691e-01  1.266820e-02
110 1.520517e-169 1.499133e-22  1.000000e+00


$weights
$weights[[1]]
$weights[[1]][[1]]
         [,1]       [,2]        [,3]     [,4]       [,5]
[1,] 5.464515 -4.6902802  3.18160381 3.226213  14.647735
[2,] 4.026408 -0.8056404 -0.01779916 4.250232  -1.678691
[3,] 3.010690 -0.9685864  0.85639443 4.097609  28.479897
[4,] 4.600509  1.9881086 -0.84874057 3.949997 -15.542719
[5,] 6.458039  1.8188755 -1.22154593 2.465668 -24.675171

$weights[[1]][[2]]
           [,1]         [,2]         [,3]
[1,]  -27.03060    1.4060917    0.9482214
[2,]  -30.58581   -0.3005019   -0.7312736
[3,] -325.23970  -65.7362794   72.5672232
[4,]    6.84409  104.4078432 -117.3263069
[5,]  -27.15391   -1.2175032    0.7291270
[6,]  114.91548 -118.9889901 -330.6643346



$generalized.weights
$generalized.weights[[1]]
           [,1]        [,2]         [,3]         [,4]         [,5]         [,6]
28          NaN         NaN          NaN          NaN -0.011822706    0.9166172
80  -36.6286038 832.6591964 -475.8968836 -736.1233504 51.898859410 -827.1743197
101   7.3494340   9.3538065  -18.6631447  -17.3370714  1.327360062    9.4965240
111  57.0558573  69.7957641 -142.0191610 -130.5385250 11.165646356   31.7301055
137  19.3121730  24.0009553  -48.4533921  -44.7257857  3.664378930   16.3468483
133  17.1946949  21.3154035  -43.0858451  -39.7442582  3.279073583   13.7509343
144  16.2054286  20.1061493  -40.6243566  -37.4822072  3.085201506   13.2142352
132  35.9700064  44.0961514  -89.6299052  -82.4318276  7.010380567   21.4101455
98   37.0602731  46.0406013  -92.9574159  -85.7941062          NaN          NaN
103  22.8938305  28.1801186  -57.1629229  -52.6295945  4.427027042   15.3277515
90   45.9362619  56.7074390 -114.8639257 -105.8369081          NaN          NaN
70   30.3115684  37.8716827  -76.2386848  -70.4667407          NaN          NaN
79   58.8634842  72.2676704 -146.7836403 -135.0488698 11.439835097   36.6159509
116  32.4627110  39.9246830  -81.0207889  -74.5784591  6.287702092   21.2308343
14          NaN         NaN          NaN          NaN -0.009086563    0.8758548
126  36.3059072  44.5465651  -90.5061838  -83.2571290  7.064055436   22.1851841
62   44.7996266  55.3588769 -112.0701340 -103.2868821          NaN          NaN
4           NaN         NaN          NaN          NaN -0.009252326    1.2756517
143  36.1873131  44.5412666  -90.3531324  -83.1867096  6.998182566   24.2002506
40          NaN         NaN          NaN          NaN -0.012068392    0.9959325
93   33.1073011  41.2431439  -83.1606573  -76.8101827          NaN          NaN
122  39.9145217  49.1006735  -99.6305759  -91.7141550  7.727598639   26.2723843
5           NaN         NaN          NaN          NaN -0.009746849    0.7729698
66   31.9419243  39.9225707  -80.3531838  -74.2765396          NaN          NaN
135  37.6549835  46.3606020  -94.0307045  -86.5790310  7.278090176   25.3730483
47          NaN         NaN          NaN          NaN -0.008941762    0.7741299
131  23.1568980  28.4473577  -57.7622380  -53.1530327  4.495164560   14.6616250
123   8.5545400  10.6432362  -21.4749118  -19.8286671  1.619591055    7.4159295
84   53.5879295  65.6631084 -133.4985322 -122.7617933 10.453524495   31.4335532
48          NaN         NaN          NaN          NaN -0.009568882    1.0784074
108  19.6584685  24.2601939  -49.1481995  -45.2817990  3.782322293   14.0920972
3           NaN         NaN          NaN          NaN -0.010899162    0.9918316
87   51.0507543  62.8739858 -127.5030757 -117.4091180  9.861002722   34.7054360
41          NaN         NaN          NaN          NaN -0.011274510    0.8717644
115  19.4772086  24.1563109  -48.8168676  -45.0364896  3.710878518   15.7458727
100  39.5776596  49.0826849  -99.1706673  -91.4798071          NaN          NaN
72   27.4795219  34.5977517  -69.3453504  -64.2098885          NaN          NaN
32          NaN         NaN          NaN          NaN -0.016620017    1.2688334
42    0.1547226   0.3413488   -0.5397788   -0.5725869 -0.016140689    2.3502212
43          NaN         NaN          NaN          NaN -0.008202895    0.9898394
2           NaN         NaN          NaN          NaN -0.014302657    1.2772341
138  42.5744955  52.2860100 -106.1818431  -97.7013553  8.269079281   26.7306495
54   50.7187793  62.4770402 -126.6860726 -116.6627683  9.793228112   34.6577036
49          NaN         NaN          NaN          NaN -0.010622304    0.7754525
102  36.1873131  44.5412666  -90.3531324  -83.1867096  6.998182566   24.2002506
56   58.3850103  71.6939202 -145.6043752 -133.9707284 11.342711570   36.5202413
51   38.0922213  47.2994338  -95.5281764  -88.1577709          NaN          NaN
6           NaN         NaN          NaN          NaN -0.011589421    0.9878510
107  51.9834446  63.8467228 -129.6536103 -119.3012594 10.094857423   32.7203308
130  50.4264157  61.6628260 -125.4940394 -115.3376002  9.875373549   27.6976178
96   32.8761535  84.0993208 -110.4079948 -116.5151552          NaN          NaN
106   9.6520231  12.0073190  -24.2285939  -22.3705747  1.827788846    8.3470245
57   57.9102829  71.1258803 -144.4349941 -132.9019812 11.246567862   36.4183327
8           NaN         NaN          NaN          NaN -0.011326546    0.9950674
26          NaN         NaN          NaN          NaN -0.014593990    1.5108870
17          NaN         NaN          NaN          NaN -0.010618902    0.7051946
63   29.9854157  37.5089265  -75.4795907  -69.7941797          NaN          NaN
97   42.1234923  52.3012168 -105.5548753  -97.3746313          NaN          NaN
22          NaN         NaN          NaN          NaN -0.011178047    0.9846209
35          NaN         NaN          NaN          NaN -0.013001761    1.2778361
117  42.1651906  51.7438426 -105.1208599  -96.7052957  8.201637524   25.8856181
149  28.7508396  35.5135882  -71.9132667  -66.2723179  5.521744474   21.0959997
119   2.8732859   3.6431166   -7.2824002   -6.7581712  0.523143688    3.5074560
86   52.3535316  65.5845438 -131.4518235 -121.4119259          NaN          NaN
142  49.7879396  60.7970874 -123.8186593 -113.7550816  9.776278549   26.0815413
10          NaN         NaN          NaN          NaN -0.012261131    1.1337715
55   56.7477403  69.6996760 -141.5378046 -130.2372406 11.019630905   35.7397768
92   55.6180580  68.3691245 -138.7780096 -127.7263151 10.782995377   35.8712830
25          NaN         NaN          NaN          NaN -0.004629804    1.3933758
88   54.2381406  66.6803408 -135.3425838 -124.5682819 10.513056734   35.0979036
50          NaN         NaN          NaN          NaN -0.012226210    0.9947409
139  63.0859889  77.2082822 -157.0654496 -144.3865517 12.334757018   35.6185139
20          NaN         NaN          NaN          NaN -0.009652439    0.8025541
140  43.9887152  53.8190072 -109.5016741 -100.6537918  8.605968877   24.5841325
94   14.5453219  69.2606321  -69.7422890  -81.7451525          NaN          NaN
71   64.2645783  78.6890779 -160.0388083 -147.1391723 12.553486653   36.8552011
61   30.1045109  37.5629073  -75.6826084  -69.9346167          NaN          NaN
104  31.1352072  38.3865725  -77.8037292  -71.6645947  6.001722056   21.7701255
109  21.7678290  26.8413747  -54.3995002  -50.1090062  4.194865569   15.2774412
27          NaN         NaN          NaN          NaN -0.012166439    1.3751894
121  24.1440334  29.7336548  -60.2994214  -55.5246957  4.664307890   16.3829799
60   46.7143165  57.6264921 -116.7616923 -107.5623481          NaN          NaN
65  -29.2660800 834.1267445 -489.3387739 -745.9069335 52.844505010 -816.6703412
36          NaN         NaN          NaN          NaN -0.012749332    0.9143518
150  49.9507738  61.2977623 -124.5305631 -114.5609776  9.716122405   30.6606214
19          NaN         NaN          NaN          NaN -0.013410190    0.9555719
9           NaN         NaN          NaN          NaN -0.008594810    1.3841522
134  63.0527281  77.1404231 -156.9550274 -144.2713979 12.336541669   35.1954802
30          NaN         NaN          NaN          NaN -0.008914240    1.2773271
52   44.7964759  55.3730544 -112.0790945 -103.3035105          NaN          NaN
95   49.0053986  60.3981609 -122.4383012 -112.7668254          NaN          NaN
38          NaN         NaN          NaN          NaN -0.008514414    0.6843843
83   26.5718315  34.0870184  -67.4625099  -62.6785584          NaN          NaN
141  19.8344598  24.4926184  -49.6036906  -45.7090825  3.811532600   14.4451014
21          NaN         NaN          NaN          NaN -0.014464758    1.1812802
105  15.4505158  19.2183318  -38.7815469  -35.8062918  2.926583341   13.3253027
113  34.6833892  42.5359976  -86.4413371  -79.5079304  6.754396833   20.8993389
13          NaN         NaN          NaN          NaN -0.012471373    1.1323837
69   65.1534566  79.6647835 -162.1377973 -149.0123610 12.761516051   35.6872817
110  15.4923207  19.2120753  -38.8272362  -35.8194372  2.952283554   12.4940291
           [,7]        [,8]          [,9]         [,10]        [,11]
28   -0.9174249  -1.3112744   0.013425282    -1.0298630    1.0305938
80  439.7760498 704.5084505 136.479044967 -2374.5126849 1308.8811044
101 -11.3101171 -14.3531229           NaN           NaN          NaN
111 -46.1701344 -51.5214516 -12.318561638   -35.3812377   51.3186300
137 -21.1863449 -25.4373095           NaN           NaN          NaN
133 -18.0667050 -21.5019269           NaN           NaN          NaN
144 -17.2795211 -20.6278134           NaN           NaN          NaN
132 -30.5015693 -34.4871669           NaN           NaN          NaN
98          NaN         NaN  -7.763418727   -34.5009670   44.7305280
103 -21.0995668 -24.3763441           NaN           NaN          NaN
90          NaN         NaN  -9.741422850   -37.0865126   49.8438824
70          NaN         NaN  -6.281126181   -31.4445189   39.7521539
79  -51.4801500 -58.6895171 -12.619409773   -40.8629128   57.2677501
116 -29.4194040 -33.8467206           NaN           NaN          NaN
14   -0.8793787  -1.2541319   0.010387113    -0.9840130    0.9877500
126 -31.3565690 -35.6296230           NaN           NaN          NaN
62          NaN         NaN  -9.485442626   -36.8123743   49.2258179
4    -1.2857422  -1.8287078   0.010730828    -1.4330879    1.4440046
143 -33.3237534 -38.4911730  -7.719050875   -27.0202125   37.0890506
40   -0.9977779  -1.4251512   0.013728487    -1.1189593    1.1208220
93          NaN         NaN  -6.894811982   -32.8343269   41.9652735
122 -36.3391964 -41.8559092  -8.523783506   -29.3307776   40.4407418
5    -0.7739287  -1.1058966   0.011074990    -0.8684631    0.8693860
66          NaN         NaN  -6.614332992   -33.3587342   42.1104829
135 -34.8649360 -40.3251690  -8.027708874   -28.3310487   38.8064076
47   -0.7761108  -1.1079904   0.010186321    -0.8697474    0.8717986
131 -20.5069944 -23.4549447           NaN           NaN          NaN
123  -9.5581360 -11.5172553           NaN           NaN          NaN
84  -44.9818861 -50.7180748 -11.532215226   -35.0646883   50.0176278
48   -1.0847623  -1.5450234   0.011001175    -1.2115410    1.2183679
108 -19.0402491 -22.2588143           NaN           NaN          NaN
3    -0.9950634  -1.4198758   0.012435545    -1.1143258    1.1177199
87  -47.5714551 -55.1072820 -10.876512847   -38.7537339   52.9527064
41   -0.8724948  -1.2470937   0.012801833    -0.9794694    0.9801227
115 -20.6330855 -24.5980985           NaN           NaN          NaN
100         NaN         NaN  -8.327611662   -34.8773819   45.7732214
72          NaN         NaN  -5.624329900   -31.4505673   38.8319764
32   -1.2696347  -1.8150055   0.018864947    -1.4256004    1.4262624
42   -2.3699391  -3.3696341   0.018770103    -2.6402557    2.6616131
43   -0.9963947  -1.4184401   0.009456995    -1.1120255    1.1190892
2    -1.2810632  -1.8283078   0.016309037    -1.4349827    1.4389860
138 -37.4794296 -42.8013644  -9.121609210   -29.8329369   41.6957688
54  -47.4385130 -55.0027618 -10.801689993   -38.7015564   52.8065210
49   -0.7753634  -1.1090016   0.012042838    -0.8712722    0.8710376
102 -33.3237534 -38.4911730  -7.719050875   -27.0202125   37.0890506
56  -51.2619316 -58.5005693 -12.512160820   -40.7580807   57.0274872
51          NaN         NaN  -7.984405865   -35.2973427   45.8319796
6    -0.9901559  -1.4137886   0.013196266    -1.1098707    1.1122421
107 -45.8438818 -52.3777364 -11.135587171   -36.5183950   51.0021148
130 -40.4627973 -45.0419137 -10.895207967   -30.8817458   44.9706765
96          NaN         NaN  -0.553775629  -148.4235008  105.4426099
106 -10.7642344 -12.9658554           NaN           NaN          NaN
57  -51.0392377 -58.3040558 -12.405645662   -40.6522349   56.7854157
8    -0.9978220  -1.4243006   0.012908847    -1.1179705    1.1208369
26   -1.5183116  -2.1640035   0.016724670    -1.6974395    1.7053716
17   -0.7039195  -1.0080155   0.012010850    -0.7923551    0.7908234
63          NaN         NaN  -6.189847906   -32.4488901   40.7056530
97          NaN         NaN  -8.872626761   -35.9834744   47.4077556
22   -0.9873833  -1.4093636   0.012740647    -1.1062330    1.1091100
35   -1.2832951  -1.8298620   0.014872572    -1.4356286    1.4414315
117 -36.5361100 -41.5510109  -9.047466256   -28.8853250   40.6397871
149 -28.3285813 -33.2472852           NaN           NaN          NaN
119  -4.2182486  -5.3186175           NaN           NaN          NaN
86          NaN         NaN -11.002526308   -42.0153395   55.1896372
142 -38.6959584 -42.6665059 -10.786417074   -29.0687949   42.9915555
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55  -50.0659275 -57.2075835 -12.155691367   -39.8884858   55.6994431
92  -49.9051484 -57.2714642 -11.894226859   -40.0429256   55.5305993
25   -1.4112162  -2.0003710   0.005673031    -1.5652137    1.5846663
88  -48.7824382 -56.0166973 -11.596490069   -39.1792776   54.2819515
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139 -51.5800910 -57.7297283 -13.608162039   -39.7217091   57.3383313
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140 -35.7160143 -39.8943904  -9.494531737   -27.4140362   39.7002204
94          NaN         NaN   4.547166266  -160.0456726  102.9925868
71  -53.1100720 -59.6231607 -13.849229496   -41.1057322   59.0459569
61          NaN         NaN  -6.247111098   -30.9822530   39.2849955
104 -29.6117537 -34.4704462           NaN           NaN          NaN
109 -20.7593368 -24.1810836           NaN           NaN          NaN
27   -1.3833378  -1.9702389   0.013989163    -1.5449611    1.5537161
121 -22.4681132 -26.0187736           NaN           NaN          NaN
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65  430.7976216 692.8183894 133.778311760 -2358.0194787 1303.4771721
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150 -43.2777111 -49.2165677 -10.718140813   -34.2136343   48.1385415
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9    -1.3968995  -1.9850134   0.010048267    -1.5549455    1.5687765
134 -51.1521129 -57.1226096 -13.610302986   -39.2464960   56.8577334
30   -1.2878669  -1.8312950   0.010358120    -1.4349619    1.4463744
52          NaN         NaN  -9.479389523   -37.0541612   49.4597498
95          NaN         NaN -10.426075808   -37.9126692   51.5356626
38   -0.6853770  -0.9792174   0.009678297    -0.7689310    0.7699068
83          NaN         NaN  -5.346329877   -32.1103377   38.4855357
141 -19.4356036 -22.7816874           NaN           NaN          NaN
21   -1.1832819  -1.6902989   0.016449505    -1.3272065    1.3292091
105 -17.1949726 -20.7034617           NaN           NaN          NaN
113 -29.6633804 -33.6173787           NaN           NaN          NaN
13   -1.1360390  -1.6210712   0.014228367    -1.2722372    1.2760741
69  -52.1813870 -58.0545717 -14.079445997   -39.7890329   57.9935975
110 -16.3816161 -19.5222083           NaN           NaN          NaN
          [,12]
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101         NaN
111   57.380094
137         NaN
133         NaN
144         NaN
132         NaN
98    53.673717
103         NaN
90    58.466358
70    48.462163
79    65.418698
116         NaN
14     1.408909
126         NaN
62    57.908863
4      2.054222
143   42.926302
40     1.601109
93    50.833228
122   46.673642
5      1.242454
66    51.385754
135   44.973939
47     1.244771
131         NaN
123         NaN
84    56.508666
48     1.735630
108         NaN
3      1.595134
87    61.463968
41     1.401099
115         NaN
100   54.455771
72    47.975762
32     2.039152
42     3.785134
43     1.593405
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138   47.712029
54    61.349398
49     1.245979
102   42.926302
56    65.211059
51    54.957740
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107   58.388167
130   50.158814
96   150.969192
106         NaN
57    65.000055
8      1.600122
26     2.431024
17     1.132562
63    49.919593
97    56.105101
22     1.583340
35     2.055681
117   46.310679
149         NaN
119         NaN
86    64.540690
142   47.495286
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55    63.772511
92    63.855567
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104         NaN
109         NaN
27     2.213302
121         NaN
60    58.142102
65  2059.488600
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150   54.854264
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30     2.057113
52    58.247209
95    60.015293
38     1.100128
83    47.866101
141         NaN
21     1.899001
105         NaN
113         NaN
13     1.821165
69    64.648315
110         NaN


$startweights
$startweights[[1]]
$startweights[[1]][[1]]
             [,1]       [,2]       [,3]       [,4]        [,5]
[1,]  1.329564791  0.6482866  0.2582618 -0.1737872 -0.19159377
[2,]  0.336472797  2.0702709 -0.3170591  0.8502323 -1.19452788
[3,]  0.006892838 -0.1533984 -0.1777900  0.6976087 -0.05315882
[4,] -0.455468738 -1.3907009 -0.1699941  0.5499974  0.25519600
[5,] -0.366523933 -0.7235818 -1.3723019 -0.4027320  1.70596401

$startweights[[1]][[2]]
           [,1]        [,2]        [,3]
[1,]  1.0015133  2.12111711  0.16698928
[2,] -0.4955834  0.41452353 -0.89626463
[3,]  0.3555503 -0.47471847  0.16818539
[4,] -1.1346080  0.06599349  0.35496826
[5,]  0.8782036 -0.50247778 -0.05210512
[6,]  0.9729168 -0.82599859 -0.19593462



$result.matrix
                                  [,1]
error                     1.003674e+00
reached.threshold         9.454904e-03
steps                     3.317000e+03
Intercept.to.1layhid1     5.464515e+00
sepal_length.to.1layhid1  4.026408e+00
sepal_width.to.1layhid1   3.010690e+00
petal_length.to.1layhid1  4.600509e+00
petal_width.to.1layhid1   6.458039e+00
Intercept.to.1layhid2    -4.690280e+00
sepal_length.to.1layhid2 -8.056404e-01
sepal_width.to.1layhid2  -9.685864e-01
petal_length.to.1layhid2  1.988109e+00
petal_width.to.1layhid2   1.818875e+00
Intercept.to.1layhid3     3.181604e+00
sepal_length.to.1layhid3 -1.779916e-02
sepal_width.to.1layhid3   8.563944e-01
petal_length.to.1layhid3 -8.487406e-01
petal_width.to.1layhid3  -1.221546e+00
Intercept.to.1layhid4     3.226213e+00
sepal_length.to.1layhid4  4.250232e+00
sepal_width.to.1layhid4   4.097609e+00
petal_length.to.1layhid4  3.949997e+00
petal_width.to.1layhid4   2.465668e+00
Intercept.to.1layhid5     1.464773e+01
sepal_length.to.1layhid5 -1.678691e+00
sepal_width.to.1layhid5   2.847990e+01
petal_length.to.1layhid5 -1.554272e+01
petal_width.to.1layhid5  -2.467517e+01
Intercept.to.setosa      -2.703060e+01
1layhid1.to.setosa       -3.058581e+01
1layhid2.to.setosa       -3.252397e+02
1layhid3.to.setosa        6.844090e+00
1layhid4.to.setosa       -2.715391e+01
1layhid5.to.setosa        1.149155e+02
Intercept.to.versicolor   1.406092e+00
1layhid1.to.versicolor   -3.005019e-01
1layhid2.to.versicolor   -6.573628e+01
1layhid3.to.versicolor    1.044078e+02
1layhid4.to.versicolor   -1.217503e+00
1layhid5.to.versicolor   -1.189890e+02
Intercept.to.virginica    9.482214e-01
1layhid1.to.virginica    -7.312736e-01
1layhid2.to.virginica     7.256722e+01
1layhid3.to.virginica    -1.173263e+02
1layhid4.to.virginica     7.291270e-01
1layhid5.to.virginica    -3.306643e+02

attr(,"class")
[1] "nn"

Code

plot(nn,rep="best")

predicting test data using nueralnet model

Code

pred=predict(nn,d2)
pred

             [,1]         [,2]          [,3]
1    1.000000e+00 1.543322e-07 2.160878e-194
7    1.000000e+00 1.236690e-07 2.771169e-194
11   1.000000e+00 1.667754e-07 1.980625e-194
12   1.000000e+00 1.161251e-07 2.974214e-194
15   1.000000e+00 2.394946e-07 1.318953e-194
16   1.000000e+00 2.182152e-07 1.464285e-194
18   1.000000e+00 1.357958e-07 2.494956e-194
23   1.000000e+00 2.183795e-07 1.463011e-194
24   1.000000e+00 4.714855e-08 8.187490e-194
29   1.000000e+00 1.411851e-07 2.388258e-194
31   1.000000e+00 8.032081e-08 4.500205e-194
33   1.000000e+00 2.341945e-07 1.352482e-194
34   1.000000e+00 2.401461e-07 1.314906e-194
37   1.000000e+00 1.665316e-07 1.983943e-194
39   1.000000e+00 1.043874e-07 3.352428e-194
44   1.000000e+00 6.184516e-08 6.036209e-194
45   1.000000e+00 9.279461e-08 3.826244e-194
46   1.000000e+00 7.568342e-08 4.811453e-194
53   1.325177e-80 1.000000e+00  2.190774e-12
58   1.344990e-33 1.000000e+00  4.975231e-70
59   4.142085e-66 1.000000e+00  8.755328e-20
64   1.001147e-88 1.000000e+00  2.994413e-12
67   1.245817e-91 1.000000e+00  2.190342e-13
68   3.263666e-52 1.000000e+00  1.045512e-30
73  3.754436e-117 8.207862e-03  9.957861e-01
74   3.872982e-80 1.000000e+00  3.701609e-16
75   7.005722e-57 1.000000e+00  4.661415e-25
76   2.879189e-59 1.000000e+00  5.588502e-23
77   2.385612e-80 1.000000e+00  7.497668e-12
78  1.525785e-108 7.834634e-01  2.160684e-01
81   3.554424e-54 1.000000e+00  9.909425e-29
82   1.042360e-48 1.000000e+00  2.631862e-32
85   3.836233e-97 1.000000e+00  3.353382e-12
89   3.171050e-59 1.000000e+00  1.517788e-28
91   1.495932e-82 1.000000e+00  8.434744e-17
99   1.000000e+00 4.529049e-16 5.323737e-179
112 4.166505e-153 1.475475e-15  1.000000e+00
114 1.920991e-159 5.094395e-17  1.000000e+00
118 5.936461e-170 1.511902e-22  1.000000e+00
120 1.872536e-140 1.520990e-10  1.000000e+00
124 4.825485e-129 5.059840e-07  9.999999e-01
125 2.372101e-159 2.493772e-17  1.000000e+00
127 7.506429e-122 5.598274e-04  9.997857e-01
128 1.158913e-124 1.087215e-03  9.995360e-01
129 1.393190e-166 8.627137e-21  1.000000e+00
136 6.178003e-168 2.574711e-23  1.000000e+00
145 1.257598e-168 5.960039e-22  1.000000e+00
146 4.225858e-153 1.460614e-16  1.000000e+00
147 1.027766e-145 1.855991e-13  1.000000e+00
148 5.702970e-144 4.242005e-12  1.000000e+00

calculating accuracy of neural net model

Code

max_col=apply(pred,1,which.max)
max_col

  1   7  11  12  15  16  18  23  24  29  31  33  34  37  39  44  45  46  53  58 
  1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   2   2 
 59  64  67  68  73  74  75  76  77  78  81  82  85  89  91  99 112 114 118 120 
  2   2   2   2   3   2   2   2   2   2   2   2   2   2   2   1   3   3   3   3 
124 125 127 128 129 136 145 146 147 148 
  3   3   3   3   3   3   3   3   3   3

Code

predr=levels(d$species)[max_col]
predr

 [1] "setosa"     "setosa"     "setosa"     "setosa"     "setosa"    
 [6] "setosa"     "setosa"     "setosa"     "setosa"     "setosa"    
[11] "setosa"     "setosa"     "setosa"     "setosa"     "setosa"    
[16] "setosa"     "setosa"     "setosa"     "versicolor" "versicolor"
[21] "versicolor" "versicolor" "versicolor" "versicolor" "virginica" 
[26] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor"
[31] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor"
[36] "setosa"     "virginica"  "virginica"  "virginica"  "virginica" 
[41] "virginica"  "virginica"  "virginica"  "virginica"  "virginica" 
[46] "virginica"  "virginica"  "virginica"  "virginica"  "virginica"

Code

t=table(predr,d2$species)
acc=sum(diag(t))/sum(t)
acc

[1] 0.96