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I've a dataset looking like this:

> head(knnresults)
   ACTIVITY_X ACTIVITY_Y ACTIVITY_Z classification
1:         40         47         62        Feeding
2:         60         74         95       Standing
3:         62         63         88       Standing
4:         60         56         82       Standing
5:         66         61         90       Standing
6:         60         53         80       Standing

With the column classification having three different categories Feeding, Standing and Foraging.

I'm now selecting an optimal k value, reason why I'm classifying 20% of the data using the other 80% as training. The classification is based on the values in the first three columns. The k value showing the highest accuracy will be selected for future classification analysis.

Here's the script I've been using for this matter:

library(ISLR)
library(caret)
library(lattice)
library(ggplot2)

# Split the data for cross validation:
indxTrain <- createDataPartition(y = knnresults$classification,p = 0.8,list = FALSE)
training <- knnresults[indxTrain,]
testing <- knnresults[-indxTrain,]

# Run k-NN:
set.seed(400)
ctrl <- trainControl(method="repeatedcv",repeats = 3)
knnFit <- train(classification ~ ., data = training, method = "knn", trControl = ctrl, preProcess = c("center","scale"),tuneLength = 20)
knnFit

#Plotting different k values against accuracy (based on repeated cross validation)
plot(knnFit)

First, let me apologize as I'm new to R and I'm unsure about the legitimacy of this script. I will be very glad to accept any suggestion of correction in case errors are spotted.

Second, how do I access a confusion matrix of classification based on this code? This is important in order to calculate performance metrics associated with the classification.

I can dput() my dataset below if that can help:

> dput(knnresults)
structure(list(ACTIVITY_X = c(40L, 60L, 62L, 60L, 66L, 60L, 57L, 
54L, 52L, 93L, 80L, 14L, 61L, 51L, 40L, 20L, 21L, 5L, 53L, 48L, 
73L, 73L, 21L, 29L, 63L, 59L, 57L, 51L, 53L, 67L, 72L, 74L, 70L, 
60L, 74L, 85L, 77L, 68L, 58L, 80L, 34L, 45L, 34L, 60L, 75L, 62L, 
66L, 51L, 53L, 48L, 62L, 62L, 57L, 5L, 1L, 12L, 23L, 5L, 4L, 
0L, 13L, 45L, 44L, 31L, 68L, 88L, 43L, 70L, 18L, 83L, 71L, 67L, 
75L, 74L, 49L, 90L, 44L, 64L, 57L, 22L, 29L, 52L, 37L, 32L, 120L, 
45L, 22L, 54L, 30L, 9L, 27L, 14L, 3L, 29L, 12L, 61L, 60L, 29L, 
15L, 7L, 6L, 0L, 2L, 0L, 4L, 1L, 7L, 0L, 0L, 0L, 0L, 0L, 1L, 
23L, 49L, 46L, 8L, 31L, 45L, 60L, 37L, 61L, 52L, 51L, 38L, 86L, 
60L, 41L, 43L, 40L, 42L, 42L, 48L, 64L, 71L, 59L, 0L, 27L, 12L, 
3L, 0L, 0L, 8L, 21L, 6L, 2L, 7L, 4L, 3L, 3L, 46L, 46L, 59L, 53L, 
37L, 44L, 39L, 49L, 37L, 47L, 17L, 36L, 32L, 33L, 26L, 12L, 8L, 
31L, 35L, 27L, 27L, 24L, 17L, 35L, 39L, 28L, 54L, 5L, 0L, 0L, 
0L, 0L, 17L, 22L, 25L, 12L, 0L, 5L, 41L, 51L, 66L, 39L, 32L, 
53L, 43L, 40L, 44L, 45L, 48L, 51L, 41L, 45L, 39L, 46L, 59L, 31L, 
5L, 24L, 18L, 5L, 15L, 13L, 0L, 26L, 0L), ACTIVITY_Y = c(47L, 
74L, 63L, 56L, 61L, 53L, 40L, 41L, 49L, 32L, 54L, 13L, 99L, 130L, 
38L, 14L, 6L, 5L, 94L, 96L, 38L, 43L, 29L, 47L, 66L, 47L, 38L, 
31L, 36L, 35L, 38L, 72L, 54L, 44L, 45L, 51L, 80L, 48L, 39L, 85L, 
42L, 39L, 37L, 75L, 36L, 45L, 32L, 35L, 41L, 26L, 99L, 163L, 
124L, 0L, 0L, 24L, 37L, 0L, 6L, 0L, 29L, 29L, 26L, 27L, 54L, 
147L, 82L, 98L, 12L, 83L, 97L, 104L, 128L, 81L, 42L, 102L, 60L, 
79L, 58L, 15L, 14L, 75L, 75L, 40L, 130L, 40L, 13L, 54L, 42L, 
7L, 10L, 3L, 0L, 15L, 8L, 75L, 55L, 26L, 18L, 1L, 13L, 0L, 0L, 
0L, 1L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 17L, 45L, 38L, 10L, 31L, 
52L, 36L, 65L, 97L, 45L, 59L, 49L, 92L, 51L, 34L, 21L, 20L, 29L, 
28L, 22L, 32L, 30L, 86L, 0L, 15L, 7L, 4L, 0L, 0L, 0L, 11L, 3L, 
0L, 1L, 3L, 1L, 0L, 72L, 62L, 98L, 55L, 26L, 39L, 28L, 81L, 20L, 
52L, 12L, 48L, 24L, 40L, 30L, 5L, 6L, 40L, 37L, 33L, 26L, 17L, 
14L, 39L, 27L, 28L, 67L, 0L, 0L, 0L, 0L, 0L, 10L, 12L, 14L, 7L, 
0L, 2L, 39L, 67L, 74L, 28L, 23L, 57L, 34L, 36L, 36L, 37L, 46L, 
43L, 73L, 65L, 31L, 64L, 128L, 17L, 3L, 12L, 17L, 0L, 9L, 7L, 
0L, 17L, 0L), ACTIVITY_Z = c(62L, 95L, 88L, 82L, 90L, 80L, 70L, 
68L, 71L, 98L, 97L, 19L, 116L, 140L, 55L, 24L, 22L, 7L, 108L, 
107L, 82L, 85L, 36L, 55L, 91L, 75L, 69L, 60L, 64L, 76L, 81L, 
103L, 88L, 74L, 87L, 99L, 111L, 83L, 70L, 117L, 54L, 60L, 50L, 
96L, 83L, 77L, 73L, 62L, 67L, 55L, 117L, 174L, 136L, 5L, 1L, 
27L, 44L, 5L, 7L, 0L, 32L, 54L, 51L, 41L, 87L, 171L, 93L, 120L, 
22L, 117L, 120L, 124L, 148L, 110L, 65L, 136L, 74L, 102L, 81L, 
27L, 32L, 91L, 84L, 51L, 177L, 60L, 26L, 76L, 52L, 11L, 29L, 
14L, 3L, 33L, 14L, 97L, 81L, 39L, 23L, 7L, 14L, 0L, 2L, 0L, 4L, 
1L, 8L, 0L, 0L, 0L, 0L, 0L, 1L, 29L, 67L, 60L, 13L, 44L, 69L, 
70L, 75L, 115L, 69L, 78L, 62L, 126L, 79L, 53L, 48L, 45L, 51L, 
50L, 53L, 72L, 77L, 104L, 0L, 31L, 14L, 5L, 0L, 0L, 8L, 24L, 
7L, 2L, 7L, 5L, 3L, 3L, 85L, 77L, 114L, 76L, 45L, 59L, 48L, 95L, 
42L, 70L, 21L, 60L, 40L, 52L, 40L, 13L, 10L, 51L, 51L, 43L, 37L, 
29L, 22L, 52L, 47L, 40L, 86L, 5L, 0L, 0L, 0L, 0L, 20L, 25L, 29L, 
14L, 0L, 5L, 57L, 84L, 99L, 48L, 39L, 78L, 55L, 54L, 57L, 58L, 
66L, 67L, 84L, 79L, 50L, 79L, 141L, 35L, 6L, 27L, 25L, 5L, 17L, 
15L, 0L, 31L, 0L), classification = c("Feeding", "Standing", 
"Standing", "Standing", "Standing", "Standing", "Feeding", "Feeding", 
"Feeding", "Standing", "Standing", "Foraging", "Standing", "Standing", 
"Feeding", "Foraging", "Foraging", "Foraging", "Standing", "Standing", 
"Standing", "Standing", "Feeding", "Feeding", "Standing", "Feeding", 
"Feeding", "Feeding", "Feeding", "Feeding", "Standing", "Standing", 
"Standing", "Feeding", "Standing", "Standing", "Standing", "Standing", 
"Feeding", "Standing", "Feeding", "Feeding", "Feeding", "Standing", 
"Standing", "Feeding", "Feeding", "Feeding", "Feeding", "Feeding", 
"Standing", "Standing", "Standing", "Foraging", "Foraging", "Foraging", 
"Feeding", "Foraging", "Foraging", "Foraging", "Foraging", "Feeding", 
"Feeding", "Feeding", "Standing", "Standing", "Standing", "Standing", 
"Foraging", "Standing", "Standing", "Standing", "Standing", "Standing", 
"Feeding", "Standing", "Feeding", "Standing", "Standing", "Foraging", 
"Foraging", "Standing", "Feeding", "Feeding", "Standing", "Feeding", 
"Foraging", "Feeding", "Feeding", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Standing", "Standing", "Feeding", 
"Foraging", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Foraging", "Feeding", "Feeding", 
"Foraging", "Feeding", "Feeding", "Feeding", "Feeding", "Standing", 
"Feeding", "Feeding", "Feeding", "Standing", "Standing", "Feeding", 
"Feeding", "Feeding", "Feeding", "Feeding", "Feeding", "Feeding", 
"Standing", "Standing", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Foraging", "Standing", "Feeding", 
"Standing", "Feeding", "Feeding", "Feeding", "Feeding", "Standing", 
"Feeding", "Feeding", "Foraging", "Feeding", "Feeding", "Feeding", 
"Feeding", "Foraging", "Foraging", "Feeding", "Feeding", "Feeding", 
"Feeding", "Foraging", "Foraging", "Feeding", "Feeding", "Feeding", 
"Standing", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Feeding", "Standing", "Standing", "Feeding", "Feeding", "Feeding", 
"Feeding", "Feeding", "Feeding", "Feeding", "Feeding", "Feeding", 
"Feeding", "Feeding", "Feeding", "Feeding", "Standing", "Feeding", 
"Foraging", "Foraging", "Foraging", "Foraging", "Foraging", "Foraging", 
"Foraging", "Foraging", "Foraging")), row.names = c(NA, -215L
), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x0000000002531ef0>)

Any input is truly appreciated!

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1 Answer 1

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You can get the confusion matrix of the train object as follows:

confusionMatrix(knnFit)

Output:

Cross-Validated (10 fold, repeated 3 times) Confusion Matrix 

(entries are percentual average cell counts across resamples)

          Reference
Prediction Feeding Foraging Standing
  Feeding     37.0      0.0      0.6
  Foraging     0.6     35.8      0.0
  Standing     1.2      0.0     24.9

Accuracy (average) : 0.9769

The documentation can be found here

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