# plot.bernoulli_naive_bayes: Plot Method for bernoulli_naive_bayes Objects In naivebayes: High Performance Implementation of the Naive Bayes Algorithm

## Description

Plot method for objects of class `"bernoulli_naive_bayes"` designed for a quick look at the class marginal distributions or class conditional distributions of 0-1 valued predictors.

## Usage

 ```1 2 3``` ```## S3 method for class 'bernoulli_naive_bayes' plot(x, which = NULL, ask = FALSE, arg.cat = list(), prob = c("marginal", "conditional"), ...) ```

## Arguments

 `x` object of class inheriting from `"bernoulli_naive_bayes"`. `which` variables to be plotted (all by default). This can be any valid indexing vector or vector containing names of variables. `ask` logical; if `TRUE`, the user is asked before each plot, see `par(ask=.)`. `arg.cat` other parameters to be passed as a named list to `mosaicplot`. `prob` character; if "marginal" then marginal distributions of predictor variables for each class are visualised and if "conditional" then the class conditional distributions of predictor variables are depicted. By default, prob="marginal". `...` not used.

## Details

Class conditional or class conditional distributions are visualised by `mosaicplot`.

The parameter `prob` controls the kind of probabilities to be visualized for each individual predictor Xi. It can take on two values:

• "marginal": P(Xi|class) * P(class)

• "conditional": P(Xi|class)

## Author(s)

Michal Majka, michalmajka@hotmail.com

`naive_bayes`,`bernoulli_naive_bayes` `predict.bernoulli_naive_bayes`, `tables`, `get_cond_dist`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```# Simulate data cols <- 10 ; rows <- 100 ; probs <- c("0" = 0.4, "1" = 0.1) M <- matrix(sample(0:1, rows * cols, TRUE, probs), nrow = rows, ncol = cols) y <- factor(sample(paste0("class", LETTERS[1:2]), rows, TRUE, prob = c(0.3,0.7))) colnames(M) <- paste0("V", seq_len(ncol(M))) laplace <- 0.5 # Train the Bernoulli Naive Bayes model bnb <- bernoulli_naive_bayes(x = M, y = y, laplace = laplace) # Visualize class marginal probabilities corresponding to the first feature plot(bnb, which = 1) # Visualize class conditional probabilities corresponding to the first feature plot(bnb, which = 1, prob = "conditional") ```