Class: AdaBoostRegressor

An AdaBoost regressor.

An AdaBoost [1] regressor is a meta-estimator that begins by fitting a regressor on the original dataset and then fits additional copies of the regressor on the same dataset but where the weights of instances are adjusted according to the error of the current prediction. As such, subsequent regressors focus more on difficult cases.

This class implements the algorithm known as AdaBoost.R2 [2].

Constructors

new AdaBoostRegressor()

new AdaBoostRegressor(opts?): AdaBoostRegressor

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.estimator`?	`any`	The base estimator from which the boosted ensemble is built. If `undefined`, then the base estimator is `DecisionTreeRegressor` initialized with `max_depth=3`.
`opts.learning_rate`?	`number`	Weight applied to each regressor at each boosting iteration. A higher learning rate increases the contribution of each regressor. There is a trade-off between the `learning_rate` and `n_estimators` parameters. Values must be in the range `(0.0, inf)`.
`opts.loss`?	`"linear"` \| `"square"` \| `"exponential"`	The loss function to use when updating the weights after each boosting iteration.
`opts.n_estimators`?	`number`	The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early. Values must be in the range `\[1, inf)`.
`opts.random_state`?	`number`	Controls the random seed given at each `estimator` at each boosting iteration. Thus, it is only used when `estimator` exposes a `random_state`. In addition, it controls the bootstrap of the weights used to train the `estimator` at each boosting iteration. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns AdaBoostRegressor

Defined in generated/ensemble/AdaBoostRegressor.ts:27

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/ensemble/AdaBoostRegressor.ts:25
`_isInitialized`	`boolean`	`false`	generated/ensemble/AdaBoostRegressor.ts:24
`_py`	`PythonBridge`	`undefined`	generated/ensemble/AdaBoostRegressor.ts:23
`id`	`string`	`undefined`	generated/ensemble/AdaBoostRegressor.ts:20
`opts`	`any`	`undefined`	generated/ensemble/AdaBoostRegressor.ts:21

Accessors

estimator_

Get Signature

get estimator_(): Promise<any>

The base estimator from which the ensemble is grown.

Returns Promise<any>

Defined in generated/ensemble/AdaBoostRegressor.ts:460

estimator_errors_

Get Signature

get estimator_errors_(): Promise<any>

Regression error for each estimator in the boosted ensemble.

Returns Promise<any>

Defined in generated/ensemble/AdaBoostRegressor.ts:541

estimator_weights_

Get Signature

get estimator_weights_(): Promise<any>

Weights for each estimator in the boosted ensemble.

Returns Promise<any>

Defined in generated/ensemble/AdaBoostRegressor.ts:514

estimators_

Get Signature

get estimators_(): Promise<any>

The collection of fitted sub-estimators.

Returns Promise<any>

Defined in generated/ensemble/AdaBoostRegressor.ts:487

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/ensemble/AdaBoostRegressor.ts:595

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/ensemble/AdaBoostRegressor.ts:568

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/ensemble/AdaBoostRegressor.ts:63

Methods

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/ensemble/AdaBoostRegressor.ts:117

fit()

fit(opts): Promise<any>

Build a boosted classifier/regressor from the training set (X, y).

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights. If `undefined`, the sample weights are initialized to 1 / n_samples.
`opts.X`?	`ArrayLike`	The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
`opts.y`?	`ArrayLike`	The target values.

Returns Promise<any>

Defined in generated/ensemble/AdaBoostRegressor.ts:134

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/ensemble/AdaBoostRegressor.ts:180

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/ensemble/AdaBoostRegressor.ts:76

predict()

predict(opts): Promise<ArrayLike>

Predict regression value for X.

The predicted regression value of an input sample is computed as the weighted median prediction of the regressors in the ensemble.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns Promise<ArrayLike>

Defined in generated/ensemble/AdaBoostRegressor.ts:212

score()

score(opts): Promise<number>

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.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`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.y`?	`ArrayLike`	True values for `X`.

Returns Promise<number>

Defined in generated/ensemble/AdaBoostRegressor.ts:248

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

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`string` \| `boolean`	Metadata routing for `sample_weight` parameter in `fit`.

Returns Promise<any>

Defined in generated/ensemble/AdaBoostRegressor.ts:296

set_score_request()

set_score_request(opts): Promise<any>

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:

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`string` \| `boolean`	Metadata routing for `sample_weight` parameter in `score`.

Returns Promise<any>

Defined in generated/ensemble/AdaBoostRegressor.ts:336

staged_predict()

staged_predict(opts): Promise<any[]>

Return staged predictions for X.

The predicted regression value of an input sample is computed as the weighted median prediction of the regressors in the ensemble.

This generator method yields the ensemble prediction after each iteration of boosting and therefore allows monitoring, such as to determine the prediction on a test set after each boost.

Parameters

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

Returns Promise<any[]>

Defined in generated/ensemble/AdaBoostRegressor.ts:376

staged_score()

staged_score(opts): Promise<number>

Return staged scores for X, y.

This generator method yields the ensemble score after each iteration of boosting and therefore allows monitoring, such as to determine the score on a test set after each boost.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.X`?	`ArrayLike`	The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
`opts.y`?	`ArrayLike`	Labels for X.

Returns Promise<number>

Defined in generated/ensemble/AdaBoostRegressor.ts:414

AdaBoostClassifier AdditiveChi2Sampler