Class: SequentialFeatureSelector

Transformer that performs Sequential Feature Selection.

This Sequential Feature Selector adds (forward selection) or removes (backward selection) features to form a feature subset in a greedy fashion. At each stage, this estimator chooses the best feature to add or remove based on the cross-validation score of an estimator. In the case of unsupervised learning, this Sequential Feature Selector looks only at the features (X), not the desired outputs (y).

Constructors

new SequentialFeatureSelector()

new SequentialFeatureSelector(opts?): SequentialFeatureSelector

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.cv`?	`number`	Determines the cross-validation splitting strategy. Possible inputs for cv are:
`opts.direction`?	`"forward"` \| `"backward"`	Whether to perform forward selection or backward selection.
`opts.estimator`?	`any`	An unfitted estimator.
`opts.n_features_to_select`?	`number` \| `"auto"`	If `"auto"`, the behaviour depends on the `tol` parameter:
`opts.n_jobs`?	`number`	Number of jobs to run in parallel. When evaluating a new feature to add or remove, the cross-validation procedure is parallel over the folds. `undefined` means 1 unless in a `joblib.parallel_backend` context. `\-1` means using all processors. See Glossary for more details.
`opts.scoring`?	`string`	A single str (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set. NOTE that when using a custom scorer, it should return a single value. If `undefined`, the estimator’s score method is used.
`opts.tol`?	`number`	If the score is not incremented by at least `tol` between two consecutive feature additions or removals, stop adding or removing. `tol` can be negative when removing features using `direction="backward"`. `tol` is required to be strictly positive when doing forward selection. It can be useful to reduce the number of features at the cost of a small decrease in the score. `tol` is enabled only when `n_features_to_select` is `"auto"`.

Returns SequentialFeatureSelector

Defined in generated/feature_selection/SequentialFeatureSelector.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/feature_selection/SequentialFeatureSelector.ts:23
`_isInitialized`	`boolean`	`false`	generated/feature_selection/SequentialFeatureSelector.ts:22
`_py`	`PythonBridge`	`undefined`	generated/feature_selection/SequentialFeatureSelector.ts:21
`id`	`string`	`undefined`	generated/feature_selection/SequentialFeatureSelector.ts:18
`opts`	`any`	`undefined`	generated/feature_selection/SequentialFeatureSelector.ts:19

Accessors

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/feature_selection/SequentialFeatureSelector.ts:480

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

Returns Promise<number>

Defined in generated/feature_selection/SequentialFeatureSelector.ts:453

n_features_to_select_

Get Signature

get n_features_to_select_(): Promise<number>

The number of features that were selected.

Returns Promise<number>

Defined in generated/feature_selection/SequentialFeatureSelector.ts:507

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/feature_selection/SequentialFeatureSelector.ts:77

support_

Get Signature

get support_(): Promise<ArrayLike>

The mask of selected features.

Returns Promise<ArrayLike>

Defined in generated/feature_selection/SequentialFeatureSelector.ts:534

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/feature_selection/SequentialFeatureSelector.ts:133

fit()

fit(opts): Promise<any>

Learn the features to select from X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	Training vectors, where `n_samples` is the number of samples and `n_features` is the number of predictors.
`opts.y`?	`ArrayLike`	Target values. This parameter may be ignored for unsupervised learning.

Returns Promise<any>

Defined in generated/feature_selection/SequentialFeatureSelector.ts:150

fit_transform()

fit_transform(opts): Promise<any[]>

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.fit_params`?	`any`	Additional fit parameters.
`opts.X`?	`ArrayLike`[]	Input samples.
`opts.y`?	`ArrayLike`	Target values (`undefined` for unsupervised transformations).