# min_prob¶

• Available in: Naïve-Bayes
• Hyperparameter: yes

## Description¶

This option specifies the minimum probability to use for observations without enough data. This option can be used, for example, if one response category has very few observations compared to the total. In this case, the conditional probability may be very low. The min_sdev and eps_prob values serve as a cutoff by setting a floor on the calculated probability.

This option defaults to 0.001 and must be at least 1e-10.

## Example¶

library(h2o)
h2o.init()

# import the cars dataset
cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# Specify model-building exercise (1:binomial, 2:multinomial)
problem <- sample(1:2,1)

# Specify response column based on predictor value and problem type
predictors <- c("displacement","power","weight","acceleration","year")
if ( problem == 1 ) { response_col <- "economy_20mpg"} else { response_col <- "cylinders" }

# Convert the response column to a factor
cars[,response_col] <- as.factor(cars[,response_col])

# Specify model parameters
laplace <- c(1)
min_prob <- c(0.1)
eps_prob <- c(0.5)

# Build the model
cars_naivebayes <- h2o.naiveBayes(x=predictors, y=response_col, training_frame=cars,
eps_prob=eps_prob, min_prob=min_prob, laplace=laplace)
print(cars_naivebayes)

# Predict on training data
cars_naivebayes.pred <- predict(cars_naivebayes, cars)

# Specify grid search parameters
grid_space <- list()
grid_space$laplace <- c(1,2) grid_space$min_prob <- c(0.1,0.2)
grid_space\$eps_prob <- c(0.5,0.6)

# Construct the grid of naive bayes models
cars_naivebayes_grid <- h2o.grid(x=predictors, y=response_col, training_frame=cars,
algorithm="naivebayes", grid_id="naiveBayes_grid_cars_test",
hyper_params=grid_space)
print(cars_naivebayes_grid)

import h2o
h2o.init()
import random
from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator

# import the cars dataset:
cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# Specify model-building exercise (1:binomial, 2:multinomial)
problem = random.sample(["binomial","multinomial"],1)

# Specify response column based on predictor value and problem type
predictors = ["displacement","power","weight","acceleration","year"]
if problem == "binomial":
response_col = "economy_20mpg"
else:
response_col = "cylinders"

# Convert the response column to a factor
cars[response_col] = cars[response_col].asfactor()

# Train the model
cars_nb = H2ONaiveBayesEstimator(min_prob=0.1, eps_prob=0.5, seed=1234)
cars_nb.train(x=predictors, y=response_col, training_frame=cars)
cars_nb.show()

# Predict on training data
cars_pred = cars_nb.predict(cars)