ComplementNB#

classsklearn.naive_bayes.ComplementNB(*,alpha=1.0,force_alpha=True,fit_prior=True,class_prior=None,norm=False)[source]#

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

The Complement Naive Bayes classifier was designed to correct the “severeassumptions” made by the standard Multinomial Naive Bayes classifier. It isparticularly suited for imbalanced data sets.

Read more in theUser Guide.

Added in version 0.20.

Parameters:

alphafloat 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_alphabool, default=True

If False and alpha is less than 1e-10, it will set alpha to1e-10. If True, alpha will remain unchanged. This may causenumerical errors if alpha is too close to 0.

Added in version 1.2.

Changed in version 1.4:The default value offorce_alpha changed toTrue.

fit_priorbool, default=True

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

class_priorarray-like of shape (n_classes,), default=None

Prior probabilities of the classes. Not used.

normbool, default=False

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

Attributes:

class_count_ndarray of shape (n_classes,)

Number of samples encountered for each class during fitting. Thisvalue 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 edgecase 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. Thisvalue 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 duringfit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Defined only whenXhas feature names that are all strings.

Added 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

>>>importnumpyasnp>>>rng=np.random.RandomState(1)>>>X=rng.randint(5,size=(6,100))>>>y=np.array([1,2,3,4,5,6])>>>fromsklearn.naive_bayesimportComplementNB>>>clf=ComplementNB()>>>clf.fit(X,y)ComplementNB()>>>print(clf.predict(X[2:3]))[3]

fit(X,y,sample_weight=None)[source]#

Fit Naive Bayes classifier according to X, y.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, wheren_samples is the number of samples andn_features is the number of features.

yarray-like of shape (n_samples,)

Target values.

sample_weightarray-like of shape (n_samples,), default=None

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

Returns:

selfobject

Returns the instance itself.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest

AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

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

Returns:

paramsdict

Parameter names mapped to their values.

partial_fit(X,y,classes=None,sample_weight=None)[source]#

Incremental fit on a batch of samples.

This method is expected to be called several times consecutivelyon different chunks of a dataset so as to implement out-of-coreor online learning.

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

This method has some performance overhead hence it is better to callpartial_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, wheren_samples is the number of samples andn_features is the number of features.

yarray-like of shape (n_samples,)

Target values.

classesarray-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 omittedin subsequent calls.

sample_weightarray-like of shape (n_samples,), default=None

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

Returns:

selfobject

Returns the instance itself.

predict(X)[source]#

Perform classification on an array of test vectors X.

Parameters:

Xarray-like of shape (n_samples, n_features)

The input samples.

Returns:

Cndarray of shape (n_samples,)

Predicted target values for X.

predict_joint_log_proba(X)[source]#

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 bylogP(x,y)=logP(y)+logP(x|y),wherelogP(y) is the class prior probability andlogP(x|y) isthe class-conditional probability.

Parameters:

Xarray-like of shape (n_samples, n_features)

The input samples.

Returns:

Cndarray of shape (n_samples, n_classes)

Returns the joint log-probability of the samples for each class inthe model. The columns correspond to the classes in sortedorder, as they appear in the attributeclasses_.

predict_log_proba(X)[source]#

Return log-probability estimates for the test vector X.

Parameters:

Xarray-like of shape (n_samples, n_features)

The input samples.

Returns:

Carray-like of shape (n_samples, n_classes)

Returns the log-probability of the samples for each class inthe model. The columns correspond to the classes in sortedorder, as they appear in the attributeclasses_.

predict_proba(X)[source]#

Return probability estimates for the test vector X.

Parameters:

Xarray-like of shape (n_samples, n_features)

The input samples.

Returns:

Carray-like of shape (n_samples, n_classes)

Returns the probability of the samples for each class inthe model. The columns correspond to the classes in sortedorder, as they appear in the attributeclasses_.

score(X,y,sample_weight=None)[source]#

Returnaccuracy on provided data and labels.

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

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels forX.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy ofself.predict(X) w.r.t.y.

set_fit_request(*,sample_weight:bool|None|str='$UNCHANGED$')→ComplementNB[source]#

Configure whether metadata should be requested to be passed to thefit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

• True: metadata is requested, and passed tofit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it tofit.

• 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 theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

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

Metadata routing forsample_weight parameter infit.

Returns:

selfobject

The updated object.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects(such asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_partial_fit_request(*,classes:bool|None|str='$UNCHANGED$',sample_weight:bool|None|str='$UNCHANGED$')→ComplementNB[source]#

Configure whether metadata should be requested to be passed to thepartial_fit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

• True: metadata is requested, and passed topartial_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 topartial_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 theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

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

Metadata routing forclasses parameter inpartial_fit.

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

Metadata routing forsample_weight parameter inpartial_fit.

Returns:

selfobject

The updated object.

set_score_request(*,sample_weight:bool|None|str='$UNCHANGED$')→ComplementNB[source]#

Configure whether metadata should be requested to be passed to thescore method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

• True: metadata is requested, and passed toscore if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it toscore.

• 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 theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

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

Metadata routing forsample_weight parameter inscore.

Returns:

selfobject

The updated object.

Gallery examples#

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