Class: BernoulliNB
Naive Bayes classifier for multivariate Bernoulli models.
Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is designed for binary/boolean features.
Read more in the User Guide.
Constructors
new BernoulliNB()
new BernoulliNB(
opts?):BernoulliNB
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | ArrayLike | Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=true, for no smoothing). |
opts.binarize? | number | Threshold for binarizing (mapping to booleans) of sample features. If undefined, input is presumed to already consist of binary vectors. |
opts.class_prior? | ArrayLike | Prior probabilities of the classes. If specified, the priors are not adjusted according to the data. |
opts.fit_prior? | boolean | Whether to learn class prior probabilities or not. If false, a uniform prior will be used. |
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. |
Returns BernoulliNB
Defined in generated/naive_bayes/BernoulliNB.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/naive_bayes/BernoulliNB.ts:23 |
_isInitialized | boolean | false | generated/naive_bayes/BernoulliNB.ts:22 |
_py | PythonBridge | undefined | generated/naive_bayes/BernoulliNB.ts:21 |
id | string | undefined | generated/naive_bayes/BernoulliNB.ts:18 |
opts | any | undefined | generated/naive_bayes/BernoulliNB.ts:19 |
Accessors
class_count_
Get Signature
get class_count_():
Promise<ArrayLike>
Number of samples encountered for each class during fitting. This value is weighted by the sample weight when provided.
Returns Promise<ArrayLike>
Defined in generated/naive_bayes/BernoulliNB.ts:556
class_log_prior_
Get Signature
get class_log_prior_():
Promise<ArrayLike>
Log probability of each class (smoothed).
Returns Promise<ArrayLike>
Defined in generated/naive_bayes/BernoulliNB.ts:581
classes_
Get Signature
get classes_():
Promise<ArrayLike>
Class labels known to the classifier
Returns Promise<ArrayLike>
Defined in generated/naive_bayes/BernoulliNB.ts:606
feature_count_
Get Signature
get feature_count_():
Promise<ArrayLike[]>
Number of samples encountered for each (class, feature) during fitting. This value is weighted by the sample weight when provided.
Returns Promise<ArrayLike[]>
Defined in generated/naive_bayes/BernoulliNB.ts:629
feature_log_prob_
Get Signature
get feature_log_prob_():
Promise<ArrayLike[]>
Empirical log probability of features given a class, P(x_i|y).
Returns Promise<ArrayLike[]>
Defined in generated/naive_bayes/BernoulliNB.ts:654
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/naive_bayes/BernoulliNB.ts:704
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/naive_bayes/BernoulliNB.ts:679
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/naive_bayes/BernoulliNB.ts:63
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/naive_bayes/BernoulliNB.ts:115
fit()
fit(
opts):Promise<any>
Fit Naive Bayes classifier according to X, y.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Weights applied to individual samples (1. for unweighted). |
opts.X? | ArrayLike | Training vectors, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | ArrayLike | Target values. |
Returns Promise<any>
Defined in generated/naive_bayes/BernoulliNB.ts:132
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/naive_bayes/BernoulliNB.ts:176
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/naive_bayes/BernoulliNB.ts:76
partial_fit()
partial_fit(
opts):Promise<any>
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
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
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.X? | ArrayLike | Training vectors, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | ArrayLike | Target values. |
Returns Promise<any>
Defined in generated/naive_bayes/BernoulliNB.ts:216
predict()
predict(
opts):Promise<ArrayLike>
Perform classification on an array of test vectors X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input samples. |
Returns Promise<ArrayLike>
Defined in generated/naive_bayes/BernoulliNB.ts:265
predict_joint_log_proba()
predict_joint_log_proba(
opts):Promise<ArrayLike[]>
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
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input samples. |
Returns Promise<ArrayLike[]>
Defined in generated/naive_bayes/BernoulliNB.ts:299
predict_log_proba()
predict_log_proba(
opts):Promise<ArrayLike[]>
Return log-probability estimates for the test vector X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input samples. |
Returns Promise<ArrayLike[]>
Defined in generated/naive_bayes/BernoulliNB.ts:333
predict_proba()
predict_proba(
opts):Promise<ArrayLike[]>
Return probability estimates for the test vector X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input samples. |
Returns Promise<ArrayLike[]>
Defined in generated/naive_bayes/BernoulliNB.ts:365
score()
score(
opts):Promise<number>
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
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.X? | ArrayLike[] | Test samples. |
opts.y? | ArrayLike | True labels for X. |
Returns Promise<number>
Defined in generated/naive_bayes/BernoulliNB.ts:399
set_fit_request()
set_fit_request(
opts):Promise<any>
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:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in fit. |
Returns Promise<any>
Defined in generated/naive_bayes/BernoulliNB.ts:445
set_partial_fit_request()
set_partial_fit_request(
opts):Promise<any>
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:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.classes? | string | boolean | Metadata routing for classes parameter in partial_fit. |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in partial_fit. |
Returns Promise<any>
Defined in generated/naive_bayes/BernoulliNB.ts:481
set_score_request()
set_score_request(
opts):Promise<any>
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:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in score. |
Returns Promise<any>
Defined in generated/naive_bayes/BernoulliNB.ts:524