ComplementNB
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.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new ComplementNB(opts?: object): ComplementNB;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | ArrayLike | Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=true, for no smoothing). Default Value 1 |
opts.class_prior? | ArrayLike | Prior probabilities of the classes. Not used. |
opts.fit_prior? | boolean | Only used in edge case with a single class in the training set. Default Value true |
opts.force_alpha? | boolean | 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. Default Value false |
opts.norm? | boolean | 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. Default Value false |
Returns
Defined in: generated/naive_bayes/ComplementNB.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/naive_bayes/ComplementNB.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/naive_bayes/ComplementNB.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/naive_bayes/ComplementNB.ts:21 (opens in a new tab)
id
string
Defined in: generated/naive_bayes/ComplementNB.ts:18 (opens in a new tab)
opts
any
Defined in: generated/naive_bayes/ComplementNB.ts:19 (opens in a new tab)
Accessors
class_count_
Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided.
Signature
class_count_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/ComplementNB.ts:438 (opens in a new tab)
class_log_prior_
Smoothed empirical log probability for each class. Only used in edge case with a single class in the training set.
Signature
class_log_prior_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/ComplementNB.ts:463 (opens in a new tab)
classes_
Class labels known to the classifier
Signature
classes_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/ComplementNB.ts:488 (opens in a new tab)
feature_all_
Number of samples encountered for each feature during fitting. This value is weighted by the sample weight when provided.
Signature
feature_all_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/ComplementNB.ts:511 (opens in a new tab)
feature_count_
Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided.
Signature
feature_count_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/naive_bayes/ComplementNB.ts:536 (opens in a new tab)
feature_log_prob_
Empirical weights for class complements.
Signature
feature_log_prob_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/naive_bayes/ComplementNB.ts:561 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/ComplementNB.ts:611 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/naive_bayes/ComplementNB.ts:586 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/naive_bayes/ComplementNB.ts:63 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/naive_bayes/ComplementNB.ts:67 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/naive_bayes/ComplementNB.ts:122 (opens in a new tab)
fit()
Fit Naive Bayes classifier according to X, y.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vectors, where n\_samples is the number of samples and n\_features is the number of features. |
opts.sample_weight? | ArrayLike | Weights applied to individual samples (1. for unweighted). |
opts.y? | ArrayLike | Target values. |
Returns
Promise<any>
Defined in: generated/naive_bayes/ComplementNB.ts:139 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/naive_bayes/ComplementNB.ts:76 (opens in a new tab)
partial_fit()
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.
Signature
partial_fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vectors, where n\_samples is the number of samples and n\_features is the number of features. |
opts.classes? | ArrayLike | 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. |
opts.sample_weight? | ArrayLike | Weights applied to individual samples (1. for unweighted). |
opts.y? | ArrayLike | Target values. |
Returns
Promise<any>
Defined in: generated/naive_bayes/ComplementNB.ts:192 (opens in a new tab)
predict()
Perform classification on an array of test vectors X.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input samples. |
Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/ComplementNB.ts:250 (opens in a new tab)
predict_joint_log_proba()
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.
Signature
predict_joint_log_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input samples. |
Returns
Promise<ArrayLike[]>
Defined in: generated/naive_bayes/ComplementNB.ts:285 (opens in a new tab)
predict_log_proba()
Return log-probability estimates for the test vector X.
Signature
predict_log_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input samples. |
Returns
Promise<ArrayLike[]>
Defined in: generated/naive_bayes/ComplementNB.ts:321 (opens in a new tab)
predict_proba()
Return probability estimates for the test vector X.
Signature
predict_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input samples. |
Returns
Promise<ArrayLike[]>
Defined in: generated/naive_bayes/ComplementNB.ts:356 (opens in a new tab)
score()
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.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True labels for X. |
Returns
Promise<number>
Defined in: generated/naive_bayes/ComplementNB.ts:391 (opens in a new tab)