Class: RFECV

Recursive feature elimination with cross-validation to select features.

The number of features selected is tuned automatically by fitting an RFE selector on the different cross-validation splits (provided by the cv parameter). The performance of the RFE selector are evaluated using scorer for different number of selected features and aggregated together. Finally, the scores are averaged across folds and the number of features selected is set to the number of features that maximize the cross-validation score. See glossary entry for cross-validation estimator.

Constructors

new RFECV()

new RFECV(opts?): RFECV

Parameters

Parameter	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.
`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`.
`opts.n_jobs`?	`number`	Number of cores to run in parallel while fitting across 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 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`.
`opts.verbose`?	`number`	Controls verbosity of output.

Returns RFECV

Defined in generated/feature_selection/RFECV.ts:25

Properties

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

Accessors

cv_results_

Get Signature

get cv_results_(): Promise<any>

All arrays (values of the dictionary) are sorted in ascending order by the number of features used (i.e., the first element of the array represents the models that used the least number of features, while the last element represents the models that used all available features). This dictionary contains the following keys:

Returns Promise<any>

Defined in generated/feature_selection/RFECV.ts:657

estimator_

Get Signature

get estimator_(): Promise<any>

The fitted estimator used to select features.

Returns Promise<any>

Defined in generated/feature_selection/RFECV.ts:634

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/RFECV.ts:726

n_features_

Get Signature

get n_features_(): Promise<number>

The number of selected features with cross-validation.

Returns Promise<number>

Defined in generated/feature_selection/RFECV.ts:680

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/RFECV.ts:703

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/RFECV.ts:82

ranking_

Get Signature

get ranking_(): Promise<any[]>

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.

Returns Promise<any[]>

Defined in generated/feature_selection/RFECV.ts:751

support_

Get Signature

get support_(): Promise<ArrayLike>

The mask of selected features.

Returns Promise<ArrayLike>

Defined in generated/feature_selection/RFECV.ts:773

Methods

decision_function()

decision_function(opts): Promise<any>

Compute the decision function of X.

Parameters

Parameter	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:150

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/RFECV.ts:133

fit()

fit(opts): Promise<any>

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

Parameters

Parameter	Type	Description
`opts`	`object`	-
`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.X`?	`ArrayLike`	Training vector, where `n_samples` is the number of samples and `n_features` is the total number of features.
`opts.y`?	`ArrayLike`	Target values (integers for classification, real numbers for regression).

Returns Promise<any>

Defined in generated/feature_selection/RFECV.ts:182

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).

Returns Promise<any[]>

Defined in generated/feature_selection/RFECV.ts:226

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

Mask feature names according to selected features.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.input_features`?	`any`	Input features.

Returns Promise<any>

Defined in generated/feature_selection/RFECV.ts:268

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Raise NotImplementedError.

This estimator does not support metadata routing yet.

Parameters

Parameter	Type
`opts`	`object`

Returns Promise<any>

Defined in generated/feature_selection/RFECV.ts:302

get_support()

get_support(opts): Promise<any>

Get a mask, or integer index, of the features selected.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.indices`?	`boolean`	If `true`, the return value will be an array of integers, rather than a boolean mask.

Returns Promise<any>

Defined in generated/feature_selection/RFECV.ts:328

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/feature_selection/RFECV.ts:95

inverse_transform()

inverse_transform(opts): Promise<any>

Reverse the transformation operation.

Parameters

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

Returns Promise<any>

Defined in generated/feature_selection/RFECV.ts:362

predict()

predict(opts): Promise<any>

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

Parameters

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

Returns Promise<any>

Defined in generated/feature_selection/RFECV.ts:394

predict_log_proba()

predict_log_proba(opts): Promise<any[]>

Predict class log-probabilities for X.

Parameters

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

Returns Promise<any[]>

Defined in generated/feature_selection/RFECV.ts:426

predict_proba()

predict_proba(opts): Promise<any[]>

Predict class probabilities for X.

Parameters

Parameter	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:458

score()

score(opts): Promise<number>

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

Parameters

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

Returns Promise<number>

Defined in generated/feature_selection/RFECV.ts:490

set_fit_request()

set_fit_request(opts): Promise<any>

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Parameters