Inheritance diagram of GNB

class mvpa2.clfs.gnb.GNB(**kwargs)

Gaussian Naive Bayes Classifier.

GNB is a probabilistic classifier relying on Bayes rule to estimate posterior probabilities of labels given the data. Naive assumption in it is an independence of the features, which allows to combine per-feature likelihoods by a simple product across likelihoods of “independent” features. See for more information.

Provided here implementation is “naive” on its own – various aspects could be improved, but it has its own advantages:

  • implementation is simple and straightforward
  • no data copying while considering samples of specific class
  • provides alternative ways to assess prior distribution of the classes in the case of unbalanced sets of samples (see parameter prior)
  • makes use of NumPy broadcasting mechanism, so should be relatively efficient
  • should work for any dimensionality of samples

GNB is listed both as linear and non-linear classifier, since specifics of separating boundary depends on the data and/or parameters: linear separation is achieved whenever samples are balanced (or prior='uniform') and features have the same variance across different classes (i.e. if common_variance=True to enforce this).

Whenever decisions are made based on log-probabilities (parameter logprob=True, which is the default), then conditional attribute values, if enabled, would also contain log-probabilities. Also mention that normalization by the evidence (P(data)) is disabled by default since it has no impact per se on classification decision. You might like to set parameter normalize to True if you want to access properly scaled probabilities in values conditional attribute.


Available conditional attributes:

  • calling_time+: None
  • estimates+: Internal classifier estimates the most recent predictions are based on
  • predicting_time+: Time (in seconds) which took classifier to predict
  • predictions+: Most recent set of predictions
  • raw_results: None
  • trained_dataset: None
  • trained_nsamples+: None
  • trained_targets+: None
  • training_stats: Confusion matrix of learning performance
  • training_time+: None

(Conditional attributes enabled by default suffixed with +)


Initialize an GNB classifier.


common_variance : bool, optional

Use the same variance across all classes. Constraints: value must be convertible to type bool. [Default: False]

prior : {laplacian_smoothing, uniform, ratio}, optional

How to compute prior distribution. Constraints: value must be one of (‘laplacian_smoothing’, ‘uniform’, ‘ratio’). [Default: ‘laplacian_smoothing’]

logprob : bool, optional

Operate on log probabilities. Preferable to avoid unneeded exponentiation and loose precision. If set, logprobs are stored in values. Constraints: value must be convertible to type bool. [Default: True]

normalize : bool, optional

Normalize (log)prob by P(data). Requires probabilities thus for logprob case would require exponentiation of ‘logprob’s, thus disabled by default since does not impact classification output. Constraints: value must be convertible to type bool. [Default: False]

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space : str, optional

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

pass_attr : str, list of str|tuple, optional

Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see Dataset.get_attr()), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance


means = None

Means of features per class

priors = None

Class probabilities

ulabels = None

Labels classifier was trained on

variances = None

Variances per class, but “vars” is taken ;)