P_iris_multiclass
Thanida Prasert
05/09/2022
Edgar Anderson’s Iris Data
Description
This famous (Fisher’s or Anderson’s) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.
Usage
iris
Format
iris is a data frame with 150 cases (rows) and 5 variables (columns) named Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species.
Source
Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179–188.
The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2–5.
Project iris
- Multiple Classification
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.8
## ✓ tidyr 1.2.0 ✓ stringr 1.4.0
## ✓ readr 2.1.2 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftlibrary(rpart)
library(rpart.plot)Explore Data
iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicaglimpse(iris)## Rows: 150
## Columns: 5
## $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
## $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
## $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
## $ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
## $ Species <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…table(iris$Species)##
## setosa versicolor virginica
## 50 50 50iris %>%
count(Species) %>%
mutate(n_percent = n/sum(n))## Species n n_percent
## 1 setosa 50 0.3333333
## 2 versicolor 50 0.3333333
## 3 virginica 50 0.3333333Plotting
ggplot(iris, aes(Petal.Length, Petal.Width)) +
geom_jitter(color = '#77aaaa') +
labs(title = "Petal Width x Petal Length") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
geom_jitter(color = '#5982e2') +
labs(title = "Sepal Width x Sepal Length") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
ggplot(iris, aes(Petal.Length, Petal.Width)) +
geom_jitter(color = '#77aaaa') +
facet_wrap(~Species)+
labs(title = "Petal Width x Petal Length of each Species") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
geom_jitter(color = '#5982e2') +
facet_wrap(~Species)+
labs(title = "Sepal Width x Sepal Length of each Species") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
When looking at sepal and petal of iris, there is only one look clear group, other petals seem hard to separate by looking at the graph. Setosa is a clearly Species when we observed. while both vericolor and virginica, they crowd together it may seem hard observing on the graph without separate Species.
Check Missing Values
mean(complete.cases(iris))## [1] 1Split Data
set.seed(55)
id <- createDataPartition(iris$Species, p = 0.8, list = FALSE)
train_df <- iris[id, ] # 80%
test_df <- iris[-id, ] # 20%
train_df## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicatest_df## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 52 6.4 3.2 4.5 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginicaTrain Model
set.seed(55)
# set control methods
control_1 <- trainControl(method = "boot",
number = 5,
verboseIter = TRUE)
control_2 <- trainControl(method = "cv",
number = 5,
verboseIter = TRUE)
# train rpart model with boot
rpart_boot <- train(
Species ~ . ,
data = train_df,
method = "rpart",
metric = "Accuracy",
trControl = control_1
)## + Resample1: cp=0
## - Resample1: cp=0
## + Resample2: cp=0
## - Resample2: cp=0
## + Resample3: cp=0
## - Resample3: cp=0
## + Resample4: cp=0
## - Resample4: cp=0
## + Resample5: cp=0
## - Resample5: cp=0
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0 on full training set# train rpart model with cv
rpart_cv <- train(
Species ~ . ,
data = train_df,
method = "rpart",
metric = "Accuracy",
trControl = control_2
)## + Fold1: cp=0
## - Fold1: cp=0
## + Fold2: cp=0
## - Fold2: cp=0
## + Fold3: cp=0
## - Fold3: cp=0
## + Fold4: cp=0
## - Fold4: cp=0
## + Fold5: cp=0
## - Fold5: cp=0
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0 on full training set# train knn model with boot
knn_boot <- train(
Species ~ . ,
data = train_df,
method = "knn",
metric = "Accuracy",
trControl = control_1
)## + Resample1: k=5
## - Resample1: k=5
## + Resample1: k=7
## - Resample1: k=7
## + Resample1: k=9
## - Resample1: k=9
## + Resample2: k=5
## - Resample2: k=5
## + Resample2: k=7
## - Resample2: k=7
## + Resample2: k=9
## - Resample2: k=9
## + Resample3: k=5
## - Resample3: k=5
## + Resample3: k=7
## - Resample3: k=7
## + Resample3: k=9
## - Resample3: k=9
## + Resample4: k=5
## - Resample4: k=5
## + Resample4: k=7
## - Resample4: k=7
## + Resample4: k=9
## - Resample4: k=9
## + Resample5: k=5
## - Resample5: k=5
## + Resample5: k=7
## - Resample5: k=7
## + Resample5: k=9
## - Resample5: k=9
## Aggregating results
## Selecting tuning parameters
## Fitting k = 5 on full training set# train knn model with cv
knn_cv <- train(
Species ~ . ,
data = train_df,
method = "knn",
metric = "Accuracy",
trControl = control_2
)## + Fold1: k=5
## - Fold1: k=5
## + Fold1: k=7
## - Fold1: k=7
## + Fold1: k=9
## - Fold1: k=9
## + Fold2: k=5
## - Fold2: k=5
## + Fold2: k=7
## - Fold2: k=7
## + Fold2: k=9
## - Fold2: k=9
## + Fold3: k=5
## - Fold3: k=5
## + Fold3: k=7
## - Fold3: k=7
## + Fold3: k=9
## - Fold3: k=9
## + Fold4: k=5
## - Fold4: k=5
## + Fold4: k=7
## - Fold4: k=7
## + Fold4: k=9
## - Fold4: k=9
## + Fold5: k=5
## - Fold5: k=5
## + Fold5: k=7
## - Fold5: k=7
## + Fold5: k=9
## - Fold5: k=9
## Aggregating results
## Selecting tuning parameters
## Fitting k = 5 on full training setrpart_boot## CART
##
## 120 samples
## 4 predictor
## 3 classes: 'setosa', 'versicolor', 'virginica'
##
## No pre-processing
## Resampling: Bootstrapped (5 reps)
## Summary of sample sizes: 120, 120, 120, 120, 120
## Resampling results across tuning parameters:
##
## cp Accuracy Kappa
## 0.00 0.9333581 0.8992098
## 0.45 0.6180129 0.4404429
## 0.50 0.6180129 0.4404429
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.rpart_cv## CART
##
## 120 samples
## 4 predictor
## 3 classes: 'setosa', 'versicolor', 'virginica'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 96, 96, 96, 96, 96
## Resampling results across tuning parameters:
##
## cp Accuracy Kappa
## 0.00 0.9583333 0.9375
## 0.45 0.8916667 0.8375
## 0.50 0.3333333 0.0000
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.knn_boot## k-Nearest Neighbors
##
## 120 samples
## 4 predictor
## 3 classes: 'setosa', 'versicolor', 'virginica'
##
## No pre-processing
## Resampling: Bootstrapped (5 reps)
## Summary of sample sizes: 120, 120, 120, 120, 120
## Resampling results across tuning parameters:
##
## k Accuracy Kappa
## 5 0.9433118 0.9118366
## 7 0.9348823 0.8992411
## 9 0.9379989 0.9044210
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was k = 5.knn_cv## k-Nearest Neighbors
##
## 120 samples
## 4 predictor
## 3 classes: 'setosa', 'versicolor', 'virginica'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 96, 96, 96, 96, 96
## Resampling results across tuning parameters:
##
## k Accuracy Kappa
## 5 0.9666667 0.9500
## 7 0.9583333 0.9375
## 9 0.9583333 0.9375
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was k = 5.Prediction test data
- It can be seen that k-Nearest Neighbors (knn) model provided the highest accuracy, 0.9666667 at k is 5.
- Model knn could be applied to test data, However, all generated models are applied to test data for looking detail.
p_rpart_boot <- predict(rpart_boot, newdata = test_df)
p_rpart_cv <- predict(rpart_cv, newdata = test_df)
p_knn_boot <- predict(knn_boot, newdata = test_df)
p_knn_cv <- predict(knn_cv, newdata = test_df)Evaluation Models
table(p_rpart_boot)## p_rpart_boot
## setosa versicolor virginica
## 10 12 8table(p_rpart_cv)## p_rpart_cv
## setosa versicolor virginica
## 10 12 8table(p_knn_boot)## p_knn_boot
## setosa versicolor virginica
## 10 11 9table(p_knn_cv)## p_knn_cv
## setosa versicolor virginica
## 10 11 9mean(p_rpart_boot == test_df$Species)## [1] 0.9333333mean(p_rpart_cv == test_df$Species)## [1] 0.9333333mean(p_knn_boot == test_df$Species)## [1] 0.9666667mean(p_knn_cv == test_df$Species)## [1] 0.9666667Sum Up
It can be seen that knn model with cross-validation provides the highest accuracy when train model and test data set. However, each model provides the accuracy more than 9 at all.
This is iris data set of R. For actual work, it may have some loss values need to be cleaned, some highly different values need to be normalized. A sample needs to gather as much as possible with depending on budget, time, and others.