The “naive_bayes” Function in R

• Package: naivebayes

• Purpose: Fit a Naive Bayes classifier to data.

• General class: Model

• Required argument(s):

• x: Predictor variables (data frame or matrix).

• y: Response variable (vector).

• Notable optional arguments:

• laplace: Laplace smoothing parameter (default is 0).

• usekernel: Boolean indicating if kernel density estimation should be used for continuous variables (default is FALSE).

• Example:

• # Load the required library
  library(naivebayes)
  
  # Example dataset
  data(iris)
  
  # Fit a Naive Bayes model
  model <- naive_bayes(Species ~ ., data = iris, laplace = 1)
  
  # Summary of the model
  print(model)
  
  # Predict on new data (although in this example, the data is not new)
  predictions <- predict(model, iris)
  
  # Print predictions
  head(predictions)

• In this example, the naive_bayes function from the naivebayes package is used to fit a Naive Bayes classifier to the iris dataset. The response variable is Species, and all other columns are used as predictor variables. Laplace smoothing is applied with a parameter value of 1. The model is then used to make predictions on the same dataset.