Cross-Validated Posterior Classification Summaries for a Naive Bayes model

Given a set of observed data including a categorical response variable y and a naiveBayes model of y, this function returns a cross validated confusion matrix by which to assess the model's posterior classification quality.

naive_classification_summary_cv(model, data, y, k = 10)

Arguments

model

a naiveBayes model object with categorical y

data

data frame including the variables in the model

y

a character string indicating the y variable in data

k

the number of folds to use for cross validation

Value

a list

Examples

data(penguins_bayes, package = "bayesrules")
example_model <- e1071::naiveBayes(species ~ bill_length_mm, data = penguins_bayes)
naive_classification_summary_cv(model = example_model, data = penguins_bayes, y = "species", k = 2)
#> $folds
#>   fold    Adelie  Chinstrap    Gentoo overall_accuracy
#> 1    1 0.9861111 0.07692308 0.8360656        0.7267442
#> 2    2 0.9125000 0.13793103 0.9047619        0.7790698
#> 
#> $cv
#>    species       Adelie  Chinstrap       Gentoo
#>     Adelie 94.74% (144)  0.00% (0)  5.26%   (8)
#>  Chinstrap  7.35%   (5) 10.29% (7) 82.35%  (56)
#>     Gentoo  8.06%  (10)  4.84% (6) 87.10% (108)
#>