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Classes
RFE

RFE

Feature ranking with recursive feature elimination.

Given an external estimator that assigns weights to features (e.g., the coefficients of a linear model), the goal of recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features. First, the estimator is trained on the initial set of features and the importance of each feature is obtained either through any specific attribute or callable. Then, the least important features are pruned from current set of features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new RFE(opts?: object): RFE;

Parameters

NameTypeDescription
opts?object-
opts.estimator?anyA supervised learning estimator with a fit method that provides information about feature importance (e.g. coef\_, feature\_importances\_).
opts.importance_getter?stringIf ‘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 (implemented with attrgetter). For example, give regressor\_.coef\_ in case of TransformedTargetRegressor or named\_steps.clf.feature\_importances\_ in case of class:~sklearn.pipeline.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.n_features_to_select?numberThe number of features to select. If undefined, half of the features are selected. If integer, the parameter is the absolute number of features to select. If float between 0 and 1, it is the fraction of features to select.
opts.step?numberIf 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. Default Value 1
opts.verbose?numberControls verbosity of output. Default Value 0

Returns

RFE

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

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

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

_py

PythonBridge

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

id

string

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

opts

any

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

Accessors

estimator_

The fitted estimator used to select features.

Signature

estimator_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/feature_selection/RFE.ts:581 (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/RFE.ts:649 (opens in a new tab)

n_features_

The number of selected features.

Signature

n_features_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/feature_selection/RFE.ts:603 (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/RFE.ts:626 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/feature_selection/RFE.ts:65 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/feature_selection/RFE.ts:69 (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<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/feature_selection/RFE.ts:672 (opens in a new tab)

support_

The mask of selected features.

Signature

support_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/feature_selection/RFE.ts:694 (opens in a new tab)

Methods

decision_function()

Compute the decision function of X.

Signature

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

Parameters

NameTypeDescription
optsobject-
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/RFE.ts:138 (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/RFE.ts:121 (opens in a new tab)

fit()

Fit the RFE model and then the underlying estimator on the selected features.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe training input samples.
opts.fit_params?anyAdditional parameters passed to the fit method of the underlying estimator.
opts.y?ArrayLikeThe target values.

Returns

Promise<any>

Defined in: generated/feature_selection/RFE.ts:171 (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

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Input samples.
opts.fit_params?anyAdditional fit parameters.
opts.y?ArrayLikeTarget values (undefined for unsupervised transformations).

Returns

Promise<any[]>

Defined in: generated/feature_selection/RFE.ts:219 (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

NameTypeDescription
optsobject-
opts.input_features?anyInput features.

Returns

Promise<any>

Defined in: generated/feature_selection/RFE.ts:266 (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

NameTypeDescription
optsobject-
opts.indices?booleanIf 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/RFE.ts:299 (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

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/feature_selection/RFE.ts:78 (opens in a new tab)

inverse_transform()

Reverse the transformation operation.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anyThe input samples.

Returns

Promise<any>

Defined in: generated/feature_selection/RFE.ts:334 (opens in a new tab)

predict()

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

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anyThe input samples.

Returns

Promise<any>

Defined in: generated/feature_selection/RFE.ts:367 (opens in a new tab)

predict_log_proba()

Predict class log-probabilities for X.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anyThe input samples.

Returns

Promise<any[]>

Defined in: generated/feature_selection/RFE.ts:400 (opens in a new tab)

predict_proba()

Predict class probabilities for X.

Signature

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

Parameters

NameTypeDescription
optsobject-
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/RFE.ts:433 (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

NameTypeDescription
optsobject-
opts.X?anyThe input samples.
opts.fit_params?anyParameters to pass to the score method of the underlying estimator.
opts.y?anyThe target values.

Returns

Promise<number>

Defined in: generated/feature_selection/RFE.ts:466 (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

NameTypeDescription
optsobject-
opts.transform?"default" | "pandas"Configure output of transform and fit\_transform.

Returns

Promise<any>

Defined in: generated/feature_selection/RFE.ts:515 (opens in a new tab)

transform()

Reduce X to the selected features.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anyThe input samples.

Returns

Promise<any>

Defined in: generated/feature_selection/RFE.ts:548 (opens in a new tab)