from .estimator_base import *
[docs]class H2ONaiveBayesEstimator(H2OEstimator):
"""
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.
Parameters
----------
laplace : int
A positive number controlling Laplace smoothing. The default zero disables smoothing.
threshold : float
The minimum standard deviation to use for observations without enough data.
Must be at least 1e-10.
eps : float
A threshold cutoff to deal with numeric instability, must be positive.
compute_metrics : bool
A logical value indicating whether model metrics should be computed. Set to FALSE
to reduce the runtime of the algorithm.
nfolds : int, optional
Number of folds for cross-validation. If nfolds >= 2, then validation must remain
empty.
fold_assignment : str
Cross-validation fold assignment scheme, if fold_column is not specified
Must be "AUTO", "Random" or "Modulo"
keep_cross_validation_predictions : bool
Whether to keep the predictions of the cross-validation models.
Returns
-------
Returns instance of H2ONaiveBayesEstimator
"""
def __init__(self,model_id=None, laplace=None, threshold=None, eps=None,
compute_metrics=None, balance_classes=None,
max_after_balance_size=None, nfolds=None, fold_assignment=None,
keep_cross_validation_predictions=None, checkpoint=None):
super(H2ONaiveBayesEstimator, self).__init__()
self._parms = locals()
self._parms = {k:v for k,v in self._parms.items() if k!="self"}
@property
def laplace(self):
return self._parms["laplace"]
@laplace.setter
def laplace(self, value):
self._parms["laplace"] = value
@property
def threshold(self):
return self._parms["threshold"]
@threshold.setter
def threshold(self, value):
self._parms["threshold"] = value
@property
def eps(self):
return self._parms["eps"]
@eps.setter
def eps(self, value):
self._parms["eps"] = value
@property
def compute_metrics(self):
return self._parms["compute_metrics"]
@compute_metrics.setter
def compute_metrics(self, value):
self._parms["compute_metrics"] = value
@property
def balance_classes(self):
return self._parms["balance_classes"]
@balance_classes.setter
def balance_classes(self, value):
self._parms["balance_classes"] = value
@property
def max_after_balance_size(self):
return self._parms["max_after_balance_size"]
@max_after_balance_size.setter
def max_after_balance_size(self, value):
self._parms["max_after_balance_size"] = value
@property
def nfolds(self):
return self._parms["nfolds"]
@nfolds.setter
def nfolds(self, value):
self._parms["nfolds"] = value
@property
def fold_assignment(self):
return self._parms["fold_assignment"]
@fold_assignment.setter
def fold_assignment(self, value):
self._parms["fold_assignment"] = value
@property
def keep_cross_validation_predictions(self):
return self._parms["keep_cross_validation_predictions"]
@keep_cross_validation_predictions.setter
def keep_cross_validation_predictions(self, value):
self._parms["keep_cross_validation_predictions"] = value
@property
def checkpoint(self):
return self._parms["checkpoint"]
@checkpoint.setter
def checkpoint(self, value):
self._parms["checkpoint"] = value