# 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")
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