CategoricalNB
Naive Bayes classifier for categorical features.
The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically distributed. The categories of each feature are drawn from a categorical distribution.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new CategoricalNB(opts?: object): CategoricalNB;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | 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. 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. 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.min_categories? | number | ArrayLike | Minimum number of categories per feature. |
Returns
Defined in: generated/naive_bayes/CategoricalNB.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/naive_bayes/CategoricalNB.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/naive_bayes/CategoricalNB.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/naive_bayes/CategoricalNB.ts:21 (opens in a new tab)
id
string
Defined in: generated/naive_bayes/CategoricalNB.ts:18 (opens in a new tab)
opts
any
Defined in: generated/naive_bayes/CategoricalNB.ts:19 (opens in a new tab)
Accessors
category_count_
Holds arrays of shape (n_classes, n_categories of respective feature) for each feature. Each array provides the number of samples encountered for each class and category of the specific feature.
Signature
category_count_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/naive_bayes/CategoricalNB.ts:436 (opens in a new tab)
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/CategoricalNB.ts:461 (opens in a new tab)
class_log_prior_
Smoothed empirical log probability for each class.
Signature
class_log_prior_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/CategoricalNB.ts:486 (opens in a new tab)
classes_
Class labels known to the classifier
Signature
classes_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/CategoricalNB.ts:511 (opens in a new tab)
feature_log_prob_
Holds arrays of shape (n_classes, n_categories of respective feature) for each feature. Each array provides the empirical log probability of categories given the respective feature and class, P(x\_i|y).
Signature
feature_log_prob_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/naive_bayes/CategoricalNB.ts:536 (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/CategoricalNB.ts:586 (opens in a new tab)
n_categories_
Number of categories for each feature. This value is inferred from the data or set by the minimum number of categories.
Signature
n_categories_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/CategoricalNB.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/CategoricalNB.ts:561 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/naive_bayes/CategoricalNB.ts:61 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/naive_bayes/CategoricalNB.ts:65 (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/CategoricalNB.ts:120 (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. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0, …, n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder. |
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/CategoricalNB.ts:137 (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/CategoricalNB.ts:74 (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. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0, …, n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder. |
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/CategoricalNB.ts:190 (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/CategoricalNB.ts:248 (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/CategoricalNB.ts:283 (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/CategoricalNB.ts:319 (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/CategoricalNB.ts:354 (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/CategoricalNB.ts:389 (opens in a new tab)