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# `sklearn.naive_bayes`.ComplementNB

class sklearn.naive_bayes.ComplementNB(*, alpha=1.0, force_alpha='warn', fit_prior=True, class_prior=None, norm=False)

  The Complement Naive Bayes classifier described in Rennie et al. (2003).

  The Complement Naive Bayes classifier was designed to correct the “severe assumptions” made by the standard Multinomial Naive Bayes classifier. It is particularly suited for imbalanced data sets.

  Read more in the User Guide.

  New in version 0.20.

  Parameters:
    alpha float or array-like of shape (n_features,), default=1.0

      Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing).

    force_alpha bool, default=False

      If False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0.

      New in version 1.2.

      Deprecated since version 1.2: The default value of `force_alpha` will change to `True` in v1.4.

    fit_prior bool, default=True

      Only used in edge case with a single class in the training set.

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

      Prior probabilities of the classes. Not used.

    norm bool, default=False

      Whether or not a second normalization of the weights is performed. The default behavior mirrors the implementations found in Mahout and Weka, which do not follow the full algorithm described in Table 9 of the paper.

  Attributes:
    class_count_ndarray of shape (n_classes,)

      Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided.

    class_log_prior_ndarray of shape (n_classes,)

      Smoothed empirical log probability for each class. Only used in edge case with a single class in the training set.

    classes_ndarray of shape (n_classes,)

      Class labels known to the classifier

    feature_all_ndarray of shape (n_features,)

      Number of samples encountered for each feature during fitting. This value is weighted by the sample weight when provided.

    feature_count_ndarray of shape (n_classes, n_features)

      Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided.

    feature_log_prob_ndarray of shape (n_classes, n_features)

      Empirical weights for class complements.

    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.

  See also

  `BernoulliNB`

    Naive Bayes classifier for multivariate Bernoulli models.

  `CategoricalNB`

    Naive Bayes classifier for categorical features.

  `GaussianNB`

    Gaussian Naive Bayes.

  `MultinomialNB`

    Naive Bayes classifier for multinomial models.

  References

  Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of naive bayes text classifiers. In ICML (Vol. 3, pp. 616-623). https://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf

  Examples

      >>> import numpy as np
      >>> rng = np.random.RandomState(1)
      >>> X = rng.randint(5, size=(6, 100))
      >>> y = np.array([1, 2, 3, 4, 5, 6])
      >>> from sklearn.naive_bayes import ComplementNB
      >>> clf = ComplementNB(force_alpha=True)
      >>> clf.fit(X, y)
      ComplementNB(force_alpha=True)
      >>> print(clf.predict(X[2:3]))
      [3]

  Methods

  `fit`(X, y[, sample_weight]) | Fit Naive Bayes classifier 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 Naive Bayes classifier according to X, y.

    Parameters:
      X{array-like, sparse matrix} 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).

    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 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, sparse matrix} 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).

    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 = '$UNCHANGED$') → ComplementNB

    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 = '$UNCHANGED$') → ComplementNB

    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 = '$UNCHANGED$') → ComplementNB

    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.ComplementNB`

Sample pipeline for text feature extraction and evaluation

Sample pipeline for text feature extraction and evaluation

Classification of text documents using sparse features

Classification of text documents using sparse features