The naive Bayes classifier assumes independence between predictor variables conditional on the response, and a Gaussian distribution of numeric predictors with mean and standard deviation computed from the training dataset. When building a naive Bayes classifier, every row in the training dataset that contains at least one NA will be skipped completely. If the test dataset has missing values, then those predictors are omitted in the probability calculation during prediction.
h2o.naiveBayes(x, y, training_frame, model_id = NULL, nfolds = 0, seed = 1, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, keep_cross_validation_models = TRUE, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, validation_frame = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_hit_ratio_k = 0, laplace = 0, threshold = 0.001, min_sdev = 0.001, eps = 0, eps_sdev = 0, min_prob = 0.001, eps_prob = 0, compute_metrics = TRUE, max_runtime_secs = 0, export_checkpoints_dir = NULL)
x  (Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. 

y  The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. 
training_frame  Id of the training data frame. 
model_id  Destination id for this model; autogenerated if not specified. 
nfolds  Number of folds for Kfold crossvalidation (0 to disable or >= 2). Defaults to 0. 
seed  Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to 1 (timebased random number). 
fold_assignment  Crossvalidation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. 
fold_column  Column with crossvalidation fold index assignment per observation. 
keep_cross_validation_models 

keep_cross_validation_predictions 

keep_cross_validation_fold_assignment 

validation_frame  Id of the validation data frame. 
ignore_const_cols 

score_each_iteration 

balance_classes 

class_sampling_factors  Desired over/undersampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. 
max_after_balance_size  Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. 
max_hit_ratio_k  Max. number (top K) of predictions to use for hit ratio computation (for multiclass only, 0 to disable) Defaults to 0. 
laplace  Laplace smoothing parameter Defaults to 0. 
threshold  This argument is deprecated, use `min_sdev` instead. The minimum standard deviation to use for observations without enough data. Must be at least 1e10. 
min_sdev  The minimum standard deviation to use for observations without enough data. Must be at least 1e10. 
eps  This argument is deprecated, use `eps_sdev` instead. A threshold cutoff to deal with numeric instability, must be positive. 
eps_sdev  A threshold cutoff to deal with numeric instability, must be positive. 
min_prob  Min. probability to use for observations with not enough data. 
eps_prob  Cutoff below which probability is replaced with min_prob. 
compute_metrics 

max_runtime_secs  Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. 
export_checkpoints_dir  Automatically export generated models to this directory. 
an object of class H2OBinomialModel if the response has two categorical levels, and H2OMultinomialModel otherwise.
# NOT RUN { h2o.init() votes_path < system.file("extdata", "housevotes.csv", package = "h2o") votes < h2o.uploadFile(path = votes_path, header = TRUE) h2o.naiveBayes(x = 2:17, y = 1, training_frame = votes, laplace = 3) # }