StackingClassifier
Stack of estimators with a final classifier.
Stacked generalization consists in stacking the output of individual estimator and use a classifier to compute the final prediction. Stacking allows to use the strength of each individual estimator by using their output as input of a final estimator.
Note that estimators\_ are fitted on the full X while final\_estimator\_ is trained using cross-validated predictions of the base estimators using cross\_val\_predict.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new StackingClassifier(opts?: object): StackingClassifier;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.cv? | number | "prefit" | Determines the cross-validation splitting strategy used in cross\_val\_predict to train final\_estimator. Possible inputs for cv are: |
opts.estimators? | any | Base estimators which will be stacked together. Each element of the list is defined as a tuple of string (i.e. name) and an estimator instance. An estimator can be set to ‘drop’ using set\_params. The type of estimator is generally expected to be a classifier. However, one can pass a regressor for some use case (e.g. ordinal regression). |
opts.final_estimator? | any | A classifier which will be used to combine the base estimators. The default classifier is a LogisticRegression. |
opts.n_jobs? | number | The number of jobs to run in parallel all estimators fit. undefined means 1 unless in a joblib.parallel\_backend context. -1 means using all processors. See Glossary for more details. |
opts.passthrough? | boolean | When false, only the predictions of estimators will be used as training data for final\_estimator. When true, the final\_estimator is trained on the predictions as well as the original training data. Default Value false |
opts.stack_method? | "auto" | "predict_proba" | "decision_function" | "predict" | Methods called for each base estimator. It can be: Default Value 'auto' |
opts.verbose? | number | Verbosity level. Default Value 0 |
Returns
Defined in: generated/ensemble/StackingClassifier.ts:27 (opens in a new tab)
Methods
decision_function()
Decision function for samples in X using the final estimator.
Signature
decision_function(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vectors, where n\_samples is the number of samples and n\_features is the number of features. |
Returns
Promise<ArrayLike>
Defined in: generated/ensemble/StackingClassifier.ts:155 (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/ensemble/StackingClassifier.ts:138 (opens in a new tab)
fit()
Fit the estimators.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vectors, where n\_samples is the number of samples and n\_features is the number of features. |
opts.sample_weight? | ArrayLike | Sample weights. If undefined, then samples are equally weighted. Note that this is supported only if all underlying estimators support sample weights. |
opts.y? | ArrayLike | Target values. Note that y will be internally encoded in numerically increasing order or lexicographic order. If the order matter (e.g. for ordinal regression), one should numerically encode the target y before calling fit. |
Returns
Promise<any>
Defined in: generated/ensemble/StackingClassifier.ts:193 (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/ensemble/StackingClassifier.ts:244 (opens in a new tab)
get_feature_names_out()
Get output feature names for transformation.
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Input features. The input feature names are only used when passthrough is true. |
Returns
Promise<any>
Defined in: generated/ensemble/StackingClassifier.ts:295 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/ensemble/StackingClassifier.ts:335 (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/ensemble/StackingClassifier.ts:88 (opens in a new tab)
predict()
Predict target for X.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vectors, where n\_samples is the number of samples and n\_features is the number of features. |
opts.predict_params? | any | Parameters to the predict called by the final\_estimator. Note that this may be used to return uncertainties from some estimators with return\_std or return\_cov. Be aware that it will only accounts for uncertainty in the final estimator. |
Returns
Promise<ArrayLike>
Defined in: generated/ensemble/StackingClassifier.ts:373 (opens in a new tab)
predict_proba()
Predict class probabilities for X using the final estimator.
Signature
predict_proba(opts: object): Promise<any[] | ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vectors, where n\_samples is the number of samples and n\_features is the number of features. |
Returns
Promise<any[] | ArrayLike[]>
Defined in: generated/ensemble/StackingClassifier.ts:415 (opens in a new tab)
score()
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True labels for X. |
Returns
Promise<number>
Defined in: generated/ensemble/StackingClassifier.ts:454 (opens in a new tab)
set_fit_request()
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:
Signature
set_fit_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in fit. |
Returns
Promise<any>
Defined in: generated/ensemble/StackingClassifier.ts:507 (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/ensemble/StackingClassifier.ts:547 (opens in a new tab)
set_score_request()
Request metadata passed to the score 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:
Signature
set_score_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in score. |
Returns
Promise<any>
Defined in: generated/ensemble/StackingClassifier.ts:586 (opens in a new tab)
transform()
Return class labels or probabilities for X for each estimator.
Signature
transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vectors, where n\_samples is the number of samples and n\_features is the number of features. |
Returns
Promise<ArrayLike[]>
Defined in: generated/ensemble/StackingClassifier.ts:624 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/ensemble/StackingClassifier.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/ensemble/StackingClassifier.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/ensemble/StackingClassifier.ts:23 (opens in a new tab)
id
string
Defined in: generated/ensemble/StackingClassifier.ts:20 (opens in a new tab)
opts
any
Defined in: generated/ensemble/StackingClassifier.ts:21 (opens in a new tab)
Accessors
classes_
Class labels.
Signature
classes_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/ensemble/StackingClassifier.ts:659 (opens in a new tab)
estimators_
The elements of the estimators parameter, having been fitted on the training data. If an estimator has been set to 'drop', it will not appear in estimators\_. When cv="prefit", estimators\_ is set to estimators and is not fitted again.
Signature
estimators_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/StackingClassifier.ts:686 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Only defined if the underlying estimators expose such an attribute when fit.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/ensemble/StackingClassifier.ts:740 (opens in a new tab)
final_estimator_
The classifier which predicts given the output of estimators\_.
Signature
final_estimator_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/StackingClassifier.ts:767 (opens in a new tab)
named_estimators_
Attribute to access any fitted sub-estimators by name.
Signature
named_estimators_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/StackingClassifier.ts:713 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/ensemble/StackingClassifier.ts:75 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/ensemble/StackingClassifier.ts:79 (opens in a new tab)
stack_method_
The method used by each base estimator.
Signature
stack_method_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/StackingClassifier.ts:794 (opens in a new tab)