StackingRegressor
Stack of estimators with a final regressor.
Stacked generalization consists in stacking the output of individual estimator and use a regressor 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 StackingRegressor(opts?: object): StackingRegressor;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. |
opts.final_estimator? | any | A regressor which will be used to combine the base estimators. The default regressor is a RidgeCV. |
opts.n_jobs? | number | The number of jobs to run in parallel for fit of all estimators. 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.verbose? | number | Verbosity level. Default Value 0 |
Returns
Defined in: generated/ensemble/StackingRegressor.ts:27 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/ensemble/StackingRegressor.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/ensemble/StackingRegressor.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/ensemble/StackingRegressor.ts:23 (opens in a new tab)
id
string
Defined in: generated/ensemble/StackingRegressor.ts:20 (opens in a new tab)
opts
any
Defined in: generated/ensemble/StackingRegressor.ts:21 (opens in a new tab)
Accessors
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/StackingRegressor.ts:445 (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/StackingRegressor.ts:499 (opens in a new tab)
final_estimator_
The regressor to stacked the base estimators fitted.
Signature
final_estimator_(): Promise<any>;Returns
Promise<any>
Defined in: generated/ensemble/StackingRegressor.ts:526 (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/StackingRegressor.ts:472 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/ensemble/StackingRegressor.ts:66 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/ensemble/StackingRegressor.ts:70 (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/StackingRegressor.ts:553 (opens in a new tab)
Methods
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/StackingRegressor.ts:125 (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. |
Returns
Promise<any>
Defined in: generated/ensemble/StackingRegressor.ts:142 (opens in a new tab)
fit_transform()
Fit the estimators and return the predictions for X for each estimator.
Signature
fit_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. |
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. |
Returns
Promise<ArrayLike[]>
Defined in: generated/ensemble/StackingRegressor.ts:191 (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/StackingRegressor.ts:242 (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/StackingRegressor.ts:79 (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/StackingRegressor.ts:280 (opens in a new tab)
score()
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y\_true \- y\_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y\_true \- y\_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n\_samples, n\_samples\_fitted), where n\_samples\_fitted is the number of samples used in the fitting for the estimator. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True values for X. |
Returns
Promise<number>
Defined in: generated/ensemble/StackingRegressor.ts:324 (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/StackingRegressor.ts:375 (opens in a new tab)
transform()
Return the predictions 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/StackingRegressor.ts:410 (opens in a new tab)