Naive Bayes in R

To implement naive Bayes classification in R, we’ll use the naiveBayes() function in the e1071 package (Meyer et al. 2021)

Models

# two models
naive_model_1 <- e1071::naiveBayes(species ~ bill_length_mm, 
                                   data = penguins)
naive_model_2 <- e1071::naiveBayes(species ~ bill_length_mm + flipper_length_mm, 
                            data = penguins)

# our penguin to classify
our_penguin <- data.frame(bill_length_mm = 50, flipper_length_mm = 195)

Predictions

predict(naive_model_1, newdata = our_penguin, type = "raw") |>
round(6)

##        Adelie Chinstrap Gentoo
## [1,] 0.000169  0.397831  0.602

predict(naive_model_1, newdata = our_penguin)

## [1] Gentoo
## Levels: Adelie Chinstrap Gentoo

predict(naive_model_2, newdata = our_penguin, type = "raw") |>
round(6)

##        Adelie Chinstrap   Gentoo
## [1,] 0.000345  0.994868 0.004787

predict(naive_model_2, newdata = our_penguin)

## [1] Chinstrap
## Levels: Adelie Chinstrap Gentoo

Validation

Confusion Matrices

penguins <- penguins %>% # keep magrittr pipe?
  mutate(class_1 = predict(naive_model_1, newdata = .),
         class_2 = predict(naive_model_2, newdata = .))

set.seed(84735)
penguins |> 
  sample_n(4) |> 
  select(bill_length_mm, flipper_length_mm, species, class_1, class_2) |> 
  rename(bill = bill_length_mm, flipper = flipper_length_mm)

## # A tibble: 4 × 5
##    bill flipper species   class_1 class_2  
##   <dbl>   <int> <fct>     <fct>   <fct>    
## 1  47.5     199 Chinstrap Gentoo  Chinstrap
## 2  40.9     214 Gentoo    Adelie  Gentoo   
## 3  41.3     194 Adelie    Adelie  Adelie   
## 4  38.5     190 Adelie    Adelie  Adelie

# Confusion matrix for naive_model_1
penguins |> 
  tabyl(species, class_1) |> 
  adorn_percentages("row") |> 
  adorn_pct_formatting(digits = 2) |>
  adorn_ns()

##    species       Adelie Chinstrap       Gentoo
##     Adelie 95.39% (145) 0.00% (0)  4.61%   (7)
##  Chinstrap  5.88%   (4) 8.82% (6) 85.29%  (58)
##     Gentoo  6.45%   (8) 4.84% (6) 88.71% (110)

accuracy: 76 percent
85 percent of Chinstap penguins are misclassified as Gentoo!

# Confusion matrix for naive_model_2
penguins |> 
  tabyl(species, class_2) |> 
  adorn_percentages("row") |> 
  adorn_pct_formatting(digits = 2) |>
  adorn_ns()

##    species       Adelie   Chinstrap       Gentoo
##     Adelie 96.05% (146)  2.63%  (4)  1.32%   (2)
##  Chinstrap  7.35%   (5) 86.76% (59)  5.88%   (4)
##     Gentoo  0.81%   (1)  0.81%  (1) 98.39% (122)

accuracy: 95 percent

Cross-Validation

# 10-fold cross-validation
set.seed(84735)
cv_model_2 <- naive_classification_summary_cv(
  model = naive_model_2, data = penguins, y = "species", k = 10)

cv_model_2$cv

##    species       Adelie   Chinstrap       Gentoo
##     Adelie 96.05% (146)  2.63%  (4)  1.32%   (2)
##  Chinstrap  7.35%   (5) 86.76% (59)  5.88%   (4)
##     Gentoo  0.81%   (1)  0.81%  (1) 98.39% (122)