Documentation
Classes
AdaBoostClassifier

AdaBoostClassifier

An AdaBoost classifier.

An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.

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

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new AdaBoostClassifier(opts?: object): AdaBoostClassifier;

Parameters

NameTypeDescription
opts?object-
opts.algorithm?"SAMME" | "SAMME.R"If ‘SAMME.R’ then use the SAMME.R real boosting algorithm. estimator must support calculation of class probabilities. If ‘SAMME’ then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations. Default Value 'SAMME.R'
opts.base_estimator?anyThe base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper classes\_ and n\_classes\_ attributes. If undefined, then the base estimator is DecisionTreeClassifier initialized with max\_depth=1.
opts.estimator?anyThe base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper classes\_ and n\_classes\_ attributes. If undefined, then the base estimator is DecisionTreeClassifier initialized with max\_depth=1.
opts.learning_rate?numberWeight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution of each classifier. 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.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. Pass an int for reproducible output across multiple function calls. See [Glossary.

Returns

AdaBoostClassifier

Defined in: generated/ensemble/AdaBoostClassifier.ts:27 (opens in a new tab)

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

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

_py

PythonBridge

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

id

string

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

opts

any

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

Accessors

classes_

The classes labels.

Signature

classes_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/ensemble/AdaBoostClassifier.ts:632 (opens in a new tab)

estimator_

The base estimator from which the ensemble is grown.

Signature

estimator_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/AdaBoostClassifier.ts:578 (opens in a new tab)

estimator_errors_

Classification error for each estimator in the boosted ensemble.

Signature

estimator_errors_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/AdaBoostClassifier.ts:713 (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/AdaBoostClassifier.ts:686 (opens in a new tab)

estimators_

The collection of fitted sub-estimators.

Signature

estimators_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/ensemble/AdaBoostClassifier.ts:605 (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/AdaBoostClassifier.ts:767 (opens in a new tab)

n_classes_

The number of classes.

Signature

n_classes_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/ensemble/AdaBoostClassifier.ts:659 (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/AdaBoostClassifier.ts:740 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

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

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

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

Methods

decision_function()

Compute the decision function of X.

Signature

decision_function(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.

Returns

Promise<any>

Defined in: generated/ensemble/AdaBoostClassifier.ts:148 (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/AdaBoostClassifier.ts:131 (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/AdaBoostClassifier.ts:186 (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/AdaBoostClassifier.ts:81 (opens in a new tab)

predict()

Predict classes for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers 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/AdaBoostClassifier.ts:237 (opens in a new tab)

predict_log_proba()

Predict class log-probabilities for X.

The predicted class log-probabilities of an input sample is computed as the weighted mean predicted class log-probabilities of the classifiers in the ensemble.

Signature

predict_log_proba(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/AdaBoostClassifier.ts:274 (opens in a new tab)

predict_proba()

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.

Signature

predict_proba(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/AdaBoostClassifier.ts:314 (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

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Test samples.
opts.sample_weight?ArrayLikeSample weights.
opts.y?ArrayLikeTrue labels for X.

Returns

Promise<number>

Defined in: generated/ensemble/AdaBoostClassifier.ts:353 (opens in a new tab)

staged_decision_function()

Compute decision function of X for each boosting iteration.

This method allows monitoring (i.e. determine error on testing set) after each boosting iteration.

Signature

staged_decision_function(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.

Returns

Promise<any[]>

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

staged_predict()

Return staged predictions for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers 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?ArrayLike[]The input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns

Promise<any[]>

Defined in: generated/ensemble/AdaBoostClassifier.ts:446 (opens in a new tab)

staged_predict_proba()

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.

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

Signature

staged_predict_proba(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.

Returns

Promise<any[]>

Defined in: generated/ensemble/AdaBoostClassifier.ts:487 (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/AdaBoostClassifier.ts:527 (opens in a new tab)