GaussianNB
Gaussian Naive Bayes (GaussianNB).
Can perform online updates to model parameters via partial\_fit. For details on algorithm used to update feature means and variance online, see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new GaussianNB(opts?: object): GaussianNB;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.priors? | ArrayLike | Prior probabilities of the classes. If specified, the priors are not adjusted according to the data. |
opts.var_smoothing? | number | Portion of the largest variance of all features that is added to variances for calculation stability. Default Value 1e-9 |
Returns
Defined in: generated/naive_bayes/GaussianNB.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/naive_bayes/GaussianNB.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/naive_bayes/GaussianNB.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/naive_bayes/GaussianNB.ts:19 (opens in a new tab)
id
string
Defined in: generated/naive_bayes/GaussianNB.ts:16 (opens in a new tab)
opts
any
Defined in: generated/naive_bayes/GaussianNB.ts:17 (opens in a new tab)
Accessors
class_count_
number of training samples observed in each class.
Signature
class_count_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/GaussianNB.ts:408 (opens in a new tab)
class_prior_
probability of each class.
Signature
class_prior_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/GaussianNB.ts:433 (opens in a new tab)
classes_
class labels known to the classifier.
Signature
classes_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/naive_bayes/GaussianNB.ts:458 (opens in a new tab)
epsilon_
absolute additive value to variances.
Signature
epsilon_(): Promise<number>;Returns
Promise<number>
Defined in: generated/naive_bayes/GaussianNB.ts:481 (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/GaussianNB.ts:529 (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/GaussianNB.ts:504 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/naive_bayes/GaussianNB.ts:40 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/naive_bayes/GaussianNB.ts:44 (opens in a new tab)
theta_
mean of each feature per class.
Signature
theta_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/naive_bayes/GaussianNB.ts:577 (opens in a new tab)
var_
Variance of each feature per class.
Signature
var_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/naive_bayes/GaussianNB.ts:554 (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/GaussianNB.ts:95 (opens in a new tab)
fit()
Fit Gaussian Naive Bayes 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/GaussianNB.ts:112 (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/GaussianNB.ts:53 (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 and numerical stability 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/GaussianNB.ts:165 (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/GaussianNB.ts:223 (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/GaussianNB.ts:258 (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/GaussianNB.ts:293 (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/GaussianNB.ts:326 (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/GaussianNB.ts:361 (opens in a new tab)