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
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.estimator? | any | A supervised learning estimator with a fit method that provides information about feature importance (e.g. coef\_, feature\_importances\_). |
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 (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? | number | The 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? | 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. Default Value 1 |
opts.verbose? | number | Controls verbosity of output. Default Value 0 |
Returns
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
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
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
| 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/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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The training input samples. |
opts.fit_params? | any | Additional parameters passed to the fit method of the underlying estimator. |
opts.y? | ArrayLike | The 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
| 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/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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Input 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
| 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/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
| Name | Type |
|---|---|
py | PythonBridge |
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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | The 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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | The 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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | The 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
| 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/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
| 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. |
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
| Name | Type | Description |
|---|---|---|
opts | object | - |
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
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | The input samples. |
Returns
Promise<any>
Defined in: generated/feature_selection/RFE.ts:548 (opens in a new tab)