sklearn.naive_bayes
.ComplementNB¶

class
sklearn.naive_bayes.
ComplementNB
(alpha=1.0, fit_prior=True, class_prior=None)[source]¶ 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.
Parameters: alpha : float, optional (default=1.0)
Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
fit_prior : boolean, optional (default=True)
Only used in edge case with a single class in the training set.
class_prior : arraylike, size (n_classes,), optional (default=None)
Prior probabilities of the classes. Not used.
Attributes
class_log_prior_ (array, shape (n_classes, )) Smoothed empirical log probability for each class. Only used in edge case with a single class in the training set. feature_log_prob_ (array, shape (n_classes, n_features)) Empirical weights for class complements. class_count_ (array, shape (n_classes,)) Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided. feature_count_ (array, 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_all_ (array, shape (n_features,)) Number of samples encountered for each feature during fitting. This value is weighted by the sample weight when provided. 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. 616623). http://people.csail.mit.edu/jrennie/papers/icml03nb.pdf
Examples
>>> import numpy as np >>> X = np.random.randint(5, size=(6, 100)) >>> y = np.array([1, 2, 3, 4, 5, 6]) >>> from sklearn.naive_bayes import ComplementNB >>> clf = ComplementNB() >>> clf.fit(X, y) ComplementNB(alpha=1.0, class_prior=None, fit_prior=True) >>> print(clf.predict(X[2:3])) [3]
Methods
fit
(X, y[, sample_weight])Fit Naive Bayes classifier according to X, y 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_log_proba
(X)Return logprobability estimates for the test vector X. predict_proba
(X)Return probability estimates for the test vector X. score
(X, y[, sample_weight])Returns the mean accuracy on the given test data and labels. set_params
(**params)Set the parameters of this estimator. 
fit
(X, y, sample_weight=None)[source]¶ Fit Naive Bayes classifier according to X, y
Parameters: X : {arraylike, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of features.
y : arraylike, shape = [n_samples]
Target values.
sample_weight : arraylike, shape = [n_samples], (default=None)
Weights applied to individual samples (1. for unweighted).
Returns: self : object
Returns self.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters: deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
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 consecutively on different chunks of a dataset so as to implement outofcore 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 : {arraylike, sparse matrix}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and n_features is the number of features.
y : arraylike, shape = [n_samples]
Target values.
classes : arraylike, 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 : arraylike, shape = [n_samples] (default=None)
Weights applied to individual samples (1. for unweighted).
Returns: self : object
Returns self.

predict
(X)[source]¶ Perform classification on an array of test vectors X.
Parameters: X : arraylike, shape = [n_samples, n_features]
Returns: C : array, shape = [n_samples]
Predicted target values for X

predict_log_proba
(X)[source]¶ Return logprobability estimates for the test vector X.
Parameters: X : arraylike, shape = [n_samples, n_features]
Returns: C : arraylike, shape = [n_samples, n_classes]
Returns the logprobability 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)[source]¶ Return probability estimates for the test vector X.
Parameters: X : arraylike, shape = [n_samples, n_features]
Returns: C : arraylike, 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)[source]¶ Returns the mean accuracy on the given test data and labels.
In multilabel 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 : arraylike, shape = (n_samples, n_features)
Test samples.
y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns: score : float
Mean accuracy of self.predict(X) wrt. y.
