``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.

Related Parameters
~~~~~~~~~~~~~~~~~~

- `eps_prob <eps_prob.html>`__

Example
~~~~~~~

.. tabs::
   .. code-tab:: r R

        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)
        print(head(cars_naivebayes_pred))

        # 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)

   .. code-tab:: python

        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)
        cars_pred.head()

        # Specify grid search parameters
        from h2o.grid.grid_search import H2OGridSearch
        hyper_params = {'laplace':[1,2], 'min_prob':[0.1,0.2], 'eps_prob':[0.5,0.6]}

        # Construct the grid of naive bayes models
        cars_nb2 = H2ONaiveBayesEstimator(seed = 1234)
        cars_grid = H2OGridSearch(model=cars_nb2, hyper_params=hyper_params)

        # Train using the grid
        cars_grid.train(x=predictors, y=response_col, training_frame=cars)
        cars_grid.show()