naive_bayes.GaussianNB
Usage
import { GaussianNB } from 'machinelearn/naive_bayes';
const nb = new GaussianNB();
const X = [[1, 20], [2, 21], [3, 22], [4, 22]];
const y = [1, 0, 1, 0];
nb.fit({ X, y });
nb.predict({ X: [[1, 20]] }); // returns [ 1 ]
Constructors
Methods
Constructors
constructor
⊕ GaussianNB()
Defined in
Parameters:
| Param | Type | Default | Description |
|---|
Returns: GaussianNB
Methods
λ fit
Defined in naive_bayes/gaussian.ts:34
Parameters:
| Param | Type | Default | Description |
|---|---|---|---|
| X | number[][] | null | array-like or sparse matrix of shape = [n_samples, n_features] |
| y | unknown | null | array-like, shape = [n_samples] or [n_samples, n_outputs] |
Returns:
void
λ fromJSON
Restore the model from saved states
Defined in naive_bayes/gaussian.ts:54
Parameters:
| Param | Type | Default | Description |
|---|---|---|---|
| options.classCategories | T[] | null | List of class categories |
| mean | number[][] | null | Mean of each feature per class |
| variance | number[][] | null | Variance of each feature per class |
Returns:
void
λ predict
Defined in naive_bayes/gaussian.ts:45
Parameters:
| Param | Type | Default | Description |
|---|---|---|---|
| X | number[][] | null | array-like, shape = [n_samples, n_features] |
Returns:
T[]
λ toJSON
Save the model's states
Defined in naive_bayes/gaussian.ts:80
Returns:
| Param | Type | Description |
|---|---|---|
| classCategories | T[] | List of class categories |