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# `sklearn.naive_bayes`.GaussianNB

class sklearn.naive_bayes.GaussianNB(*, priors=None, var_smoothing=1e-09)

  Gaussian Naive Bayes (GaussianNB).

  Can perform online updates to model parameters via `partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:

  > http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf

  Read more in the User Guide.

  Parameters:
    priors array-like of shape (n_classes,), default=None

      Prior probabilities of the classes. If specified, the priors are not adjusted according to the data.

    var_smoothing float, default=1e-9

      Portion of the largest variance of all features that is added to variances for calculation stability.

      New in version 0.20.

  Attributes:
    class_count_ndarray of shape (n_classes,)

      number of training samples observed in each class.

    class_prior_ndarray of shape (n_classes,)

      probability of each class.

    classes_ndarray of shape (n_classes,)

      class labels known to the classifier.

    epsilon_float

      absolute additive value to variances.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

    var_ndarray of shape (n_classes, n_features)

      Variance of each feature per class.

      New in version 1.0.

    theta_ndarray of shape (n_classes, n_features)

      mean of each feature per class.

  See also

  `BernoulliNB`

    Naive Bayes classifier for multivariate Bernoulli models.

  `CategoricalNB`

    Naive Bayes classifier for categorical features.

  `ComplementNB`

    Complement Naive Bayes classifier.

  `MultinomialNB`

    Naive Bayes classifier for multinomial models.

  Examples

      >>> import numpy as np
      >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
      >>> Y = np.array([1, 1, 1, 2, 2, 2])
      >>> from sklearn.naive_bayes import GaussianNB
      >>> clf = GaussianNB()
      >>> clf.fit(X, Y)
      GaussianNB()
      >>> print(clf.predict([[-0.8, -1]]))
      [1]
      >>> clf_pf = GaussianNB()
      >>> clf_pf.partial_fit(X, Y, np.unique(Y))
      GaussianNB()
      >>> print(clf_pf.predict([[-0.8, -1]]))
      [1]

  Methods

  `fit`(X, y[, sample_weight]) | Fit Gaussian Naive Bayes according to X, y.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `partial_fit`(X, y[, classes, sample_weight]) | Incremental fit on a batch of samples.
  `predict`(X) | Perform classification on an array of test vectors X.
  `predict_joint_log_proba`(X) | Return joint log probability estimates for the test vector X.
  `predict_log_proba`(X) | Return log-probability estimates for the test vector X.
  `predict_proba`(X) | Return probability estimates for the test vector X.
  `score`(X, y[, sample_weight]) | Return the mean accuracy on the given test data and labels.
  `set_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_partial_fit_request`(*[, classes, ...]) | Request metadata passed to the `partial_fit` method.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  fit(X, y, sample_weight=None)

    Fit Gaussian Naive Bayes according to X, y.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features.

      y array-like of shape (n_samples,)

        Target values.

      sample_weight array-like of shape (n_samples,), default=None

        Weights applied to individual samples (1. for unweighted).

        New in version 0.17: Gaussian Naive Bayes supports fitting with sample_weight.

    Returns:
      self object

        Returns the instance itself.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  partial_fit(X, y, classes=None, sample_weight=None)

    Incremental fit on a batch of samples.

    This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning.

    This is especially useful when the whole dataset is too big to fit in memory at once.

    This method has some performance and numerical stability overhead, hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features.

      y array-like of shape (n_samples,)

        Target values.

      classes array-like of shape (n_classes,), default=None

        List of all the classes that can possibly appear in the y vector.

        Must be provided at the first call to partial_fit, can be omitted in subsequent calls.

      sample_weight array-like of shape (n_samples,), default=None

        Weights applied to individual samples (1. for unweighted).

        New in version 0.17.

    Returns:
      self object

        Returns the instance itself.

  predict(X)

    Perform classification on an array of test vectors X.

    Parameters:
      X array-like of shape (n_samples, n_features)

        The input samples.

    Returns:
      C ndarray of shape (n_samples,)

        Predicted target values for X.

  predict_joint_log_proba(X)

    Return joint log probability estimates for the test vector X.

    For each row x of X and class y, the joint log probability is given by `log P(x, y) = log P(y) + log P(x|y),` where `log P(y)` is the class prior probability and `log P(x|y)` is the class-conditional probability.

    Parameters:
      X array-like of shape (n_samples, n_features)

        The input samples.

    Returns:
      C ndarray of shape (n_samples, n_classes)

        Returns the joint log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

  predict_log_proba(X)

    Return log-probability estimates for the test vector X.

    Parameters:
      X array-like of shape (n_samples, n_features)

        The input samples.

    Returns:
      C array-like of shape (n_samples, n_classes)

        Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

  predict_proba(X)

    Return probability estimates for the test vector X.

    Parameters:
      X array-like of shape (n_samples, n_features)

        The input samples.

    Returns:
      C array-like of shape (n_samples, n_classes)

        Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

  score(X, y, sample_weight=None)

    Return the mean accuracy on the given test data and labels.

    In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Test samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True labels for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

        Mean accuracy of `self.predict(X)` w.r.t. `y`.

  set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → GaussianNB

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  set_partial_fit_request(*, classes: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → GaussianNB

    Request metadata passed to the `partial_fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `partial_fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `partial_fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      classes str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `classes` parameter in `partial_fit`.

      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `partial_fit`.

    Returns:
      self object

        The updated object.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → GaussianNB

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

## Examples using `sklearn.naive_bayes.GaussianNB`

Comparison of Calibration of Classifiers

Comparison of Calibration of Classifiers

Probability Calibration curves

Probability Calibration curves

Probability calibration of classifiers

Probability calibration of classifiers

Classifier comparison

Classifier comparison

Plot class probabilities calculated by the VotingClassifier

Plot class probabilities calculated by the VotingClassifier

Plotting Learning Curves and Checking Models’ Scalability

Plotting Learning Curves and Checking Models' Scalability