Usage
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_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, eps = 0, compute_metrics = TRUE, max_runtime_secs = 0)
Arguments
x
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 of the response variable in the model.If the data does not contain a header, this is the column index
number starting at 0, and increasing from left to right. (The response must be either an integer or a
categorical variable).
training_frame
Id of the training data frame (Not required, to allow initial validation of model parameters).
model_id
Destination id for this model; auto-generated if not specified.
nfolds
Number of folds for N-fold cross-validation (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 (time-based random number).
fold_assignment
Cross-validation 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 cross-validation fold index assignment per observation.
keep_cross_validation_predictions
Logical
. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
keep_cross_validation_fold_assignment
Logical
. Whether to keep the cross-validation fold assignment. Defaults to FALSE.
validation_frame
Id of the validation data frame.
ignore_const_cols
Logical
. Ignore constant columns. Defaults to TRUE.
score_each_iteration
Logical
. Whether to score during each iteration of model training. Defaults to FALSE.
balance_classes
Logical
. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to
FALSE.
class_sampling_factors
Desired over/under-sampling 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 multi-class only, 0 to disable)
Defaults to 0.
laplace
Laplace smoothing parameter Defaults to 0.
threshold
The minimum standard deviation to use for observations without enough data.
Must be at least 1e-10.
eps
A threshold cutoff to deal with numeric instability, must be positive.
compute_metrics
Logical
. Compute metrics on training data Defaults to TRUE.
max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.