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AdaBoostRegressor

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].

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new AdaBoostRegressor(opts?: object): AdaBoostRegressor;

Parameters

NameTypeDescription
opts?object-
opts.base_estimator?anyThe base estimator from which the boosted ensemble is built. If undefined, then the base estimator is DecisionTreeRegressor initialized with max\_depth=3.
opts.estimator?anyThe 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?numberWeight 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). Default Value 1
opts.loss?"linear" | "square" | "exponential"The loss function to use when updating the weights after each boosting iteration. Default Value 'linear'
opts.n_estimators?numberThe 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). Default Value 50
opts.random_state?numberControls 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 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/ensemble/AdaBoostRegressor.ts:25 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/ensemble/AdaBoostRegressor.ts:24 (opens in a new tab)

_py

PythonBridge

Defined in: generated/ensemble/AdaBoostRegressor.ts:23 (opens in a new tab)

id

string

Defined in: generated/ensemble/AdaBoostRegressor.ts:20 (opens in a new tab)

opts

any

Defined in: generated/ensemble/AdaBoostRegressor.ts:21 (opens in a new tab)

Accessors

estimator_

The base estimator from which the ensemble is grown.

Signature

estimator_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/AdaBoostRegressor.ts:377 (opens in a new tab)

estimator_errors_

Regression error for each estimator in the boosted ensemble.

Signature

estimator_errors_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/AdaBoostRegressor.ts:458 (opens in a new tab)

estimator_weights_

Weights for each estimator in the boosted ensemble.

Signature

estimator_weights_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/AdaBoostRegressor.ts:431 (opens in a new tab)

estimators_

The collection of fitted sub-estimators.

Signature

estimators_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/AdaBoostRegressor.ts:404 (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/ensemble/AdaBoostRegressor.ts:512 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/ensemble/AdaBoostRegressor.ts:485 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/ensemble/AdaBoostRegressor.ts:68 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/ensemble/AdaBoostRegressor.ts:72 (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/AdaBoostRegressor.ts:129 (opens in a new tab)

fit()

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

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
opts.sample_weight?ArrayLikeSample weights. If undefined, the sample weights are initialized to 1 / n_samples.
opts.y?ArrayLikeThe target values.

Returns

Promise<any>

Defined in: generated/ensemble/AdaBoostRegressor.ts:146 (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/ensemble/AdaBoostRegressor.ts:81 (opens in a new tab)

predict()

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.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe 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:197 (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

NameTypeDescription
optsobject-
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?ArrayLikeSample weights.
opts.y?ArrayLikeTrue values for X.

Returns

Promise<number>

Defined in: generated/ensemble/AdaBoostRegressor.ts:234 (opens in a new tab)

staged_predict()

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.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe training input samples.

Returns

Promise<any[]>

Defined in: generated/ensemble/AdaBoostRegressor.ts:287 (opens in a new tab)

staged_score()

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.

Signature

staged_score(opts: object): Promise<number>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
opts.sample_weight?ArrayLikeSample weights.
opts.y?ArrayLikeLabels for X.

Returns

Promise<number>

Defined in: generated/ensemble/AdaBoostRegressor.ts:326 (opens in a new tab)