RFECV

Recursive feature elimination with cross-validation to select features.

See glossary entry for cross-validation estimator.

Constructors

constructor()

Signature

new RFECV(opts?: object): RFECV;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.cv?`	`number`	Determines the cross-validation splitting strategy. Possible inputs for cv are:
`opts.estimator?`	`any`	A supervised learning estimator with a `fit` method that provides information about feature importance either through a `coef\_` attribute or through a `feature\_importances\_` attribute.
`opts.importance_getter?`	`string`	If ‘auto’, uses the feature importance either through a `coef\_` or `feature\_importances\_` attributes of estimator. Also accepts a string that specifies an attribute name/path for extracting feature importance. For example, give `regressor\_.coef\_` in case of `TransformedTargetRegressor` or `named\_steps.clf.feature\_importances\_` in case of `Pipeline` with its last step named `clf`. If `callable`, overrides the default feature importance getter. The callable is passed with the fitted estimator and it should return importance for each feature. `Default Value` `'auto'`
`opts.min_features_to_select?`	`number`	The minimum number of features to be selected. This number of features will always be scored, even if the difference between the original feature count and `min\_features\_to\_select` isn’t divisible by `step`. `Default Value` `1`
`opts.n_jobs?`	`number`	Number of cores to run in parallel while fitting across folds. `undefined` means 1 unless in a `joblib.parallel\_backend` (opens in a new tab) context. `\-1` means using all processors. See Glossary for more details.
`opts.scoring?`	`string`	A string (see model evaluation documentation) or a scorer callable object / function with signature `scorer(estimator, X, y)`.
`opts.step?`	`number`	If greater than or equal to 1, then `step` corresponds to the (integer) number of features to remove at each iteration. If within (0.0, 1.0), then `step` corresponds to the percentage (rounded down) of features to remove at each iteration. Note that the last iteration may remove fewer than `step` features in order to reach `min\_features\_to\_select`. `Default Value` `1`
`opts.verbose?`	`number`	Controls verbosity of output. `Default Value` `0`

Returns

RFECV

Defined in: generated/feature_selection/RFECV.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/feature_selection/RFECV.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/feature_selection/RFECV.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/feature_selection/RFECV.ts:21 (opens in a new tab)

id

string

Defined in: generated/feature_selection/RFECV.ts:18 (opens in a new tab)

opts

any

Defined in: generated/feature_selection/RFECV.ts:19 (opens in a new tab)

Accessors

cv_results_

A dict with keys:

Signature

cv_results_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/feature_selection/RFECV.ts:624 (opens in a new tab)

estimator_

The fitted estimator used to select features.

Signature

estimator_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/feature_selection/RFECV.ts:601 (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/feature_selection/RFECV.ts:693 (opens in a new tab)

n_features_

The number of selected features with cross-validation.

Signature

n_features_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/feature_selection/RFECV.ts:647 (opens in a new tab)

n_features_in_

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

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/feature_selection/RFECV.ts:670 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/feature_selection/RFECV.ts:82 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/feature_selection/RFECV.ts:86 (opens in a new tab)

ranking_

The feature ranking, such that ranking\_\[i\] corresponds to the ranking position of the i-th feature. Selected (i.e., estimated best) features are assigned rank 1.

Signature

ranking_(): Promise<any[]>;

Returns

Promise<any[]>

Defined in: generated/feature_selection/RFECV.ts:718 (opens in a new tab)

support_

The mask of selected features.

Signature

support_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/feature_selection/RFECV.ts:740 (opens in a new tab)

Methods

decision_function()

Compute the decision function of X.

Signature

decision_function(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`[]	The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr\_matrix`.

Returns

Promise<any>

Defined in: generated/feature_selection/RFECV.ts:157 (opens in a new tab)

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/RFECV.ts:140 (opens in a new tab)

fit()

Fit the RFE model and automatically tune the number of selected features.

Signature

fit(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Training vector, where `n\_samples` is the number of samples and `n\_features` is the total number of features.
`opts.groups?`	`ArrayLike`	Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g., `GroupKFold`).
`opts.y?`	`ArrayLike`	Target values (integers for classification, real numbers for regression).

Returns

Promise<any>

Defined in: generated/feature_selection/RFECV.ts:190 (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/RFECV.ts:239 (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/RFECV.ts:286 (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/RFECV.ts:319 (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/RFECV.ts:95 (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/RFECV.ts:354 (opens in a new tab)

predict()

Reduce X to the selected features and predict using the estimator.

Signature

predict(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	The input samples.

Returns

Promise<any>

Defined in: generated/feature_selection/RFECV.ts:387 (opens in a new tab)

predict_log_proba()

Predict class log-probabilities for X.

Signature

predict_log_proba(opts: object): Promise<any[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	The input samples.

Returns

Promise<any[]>

Defined in: generated/feature_selection/RFECV.ts:420 (opens in a new tab)

predict_proba()

Predict class probabilities for X.

Signature

predict_proba(opts: object): Promise<any[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`[]	The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr\_matrix`.

Returns

Promise<any[]>

Defined in: generated/feature_selection/RFECV.ts:453 (opens in a new tab)

score()

Reduce X to the selected features and return the score of the estimator.

Signature

score(opts: object): Promise<number>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	The input samples.
`opts.fit_params?`	`any`	Parameters to pass to the `score` method of the underlying estimator.
`opts.y?`	`any`	The target values.