VarianceThreshold
Feature selector that removes all low-variance features.
This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new VarianceThreshold(opts?: object): VarianceThreshold;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.threshold? | number | Features with a training-set variance lower than this threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples. Default Value 0 |
Returns
Defined in: generated/feature_selection/VarianceThreshold.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/feature_selection/VarianceThreshold.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/feature_selection/VarianceThreshold.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/feature_selection/VarianceThreshold.ts:21 (opens in a new tab)
id
string
Defined in: generated/feature_selection/VarianceThreshold.ts:18 (opens in a new tab)
opts
any
Defined in: generated/feature_selection/VarianceThreshold.ts:19 (opens in a new tab)
Accessors
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/feature_selection/VarianceThreshold.ts:441 (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/feature_selection/VarianceThreshold.ts:414 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/feature_selection/VarianceThreshold.ts:37 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/feature_selection/VarianceThreshold.ts:41 (opens in a new tab)
variances_
Variances of individual features.
Signature
variances_(): Promise<any>;Returns
Promise<any>
Defined in: generated/feature_selection/VarianceThreshold.ts:387 (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/feature_selection/VarianceThreshold.ts:92 (opens in a new tab)
fit()
Learn empirical variances from X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Data from which to compute variances, where n\_samples is the number of samples and n\_features is the number of features. |
opts.y? | any | Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline. |
Returns
Promise<any>
Defined in: generated/feature_selection/VarianceThreshold.ts:109 (opens in a new tab)
fit_transform()
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.
Signature
fit_transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input samples. |
opts.fit_params? | any | Additional fit parameters. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns
Promise<any[]>
Defined in: generated/feature_selection/VarianceThreshold.ts:151 (opens in a new tab)
get_feature_names_out()
Mask feature names according to selected features.
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Input features. |
Returns
Promise<any>
Defined in: generated/feature_selection/VarianceThreshold.ts:202 (opens in a new tab)
get_support()
Get a mask, or integer index, of the features selected.
Signature
get_support(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.indices? | boolean | If true, the return value will be an array of integers, rather than a boolean mask. Default Value false |
Returns
Promise<any>
Defined in: generated/feature_selection/VarianceThreshold.ts:240 (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/feature_selection/VarianceThreshold.ts:50 (opens in a new tab)
inverse_transform()
Reverse the transformation operation.
Signature
inverse_transform(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | The input samples. |
Returns
Promise<any>
Defined in: generated/feature_selection/VarianceThreshold.ts:277 (opens in a new tab)
set_output()
Set output container.
See Introducing the set_output API for an example on how to use the API.
Signature
set_output(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | Configure output of transform and fit\_transform. |
Returns
Promise<any>
Defined in: generated/feature_selection/VarianceThreshold.ts:317 (opens in a new tab)
transform()
Reduce X to the selected features.
Signature
transform(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | The input samples. |
Returns
Promise<any>
Defined in: generated/feature_selection/VarianceThreshold.ts:352 (opens in a new tab)