adaboost

class Adaboost(random_state=None, n_estimators=50, base_estimator=None, base_estimator_config=None, **kwargs)

This class is an adaptation of scikit-learn’s AdaBoostClassifier

fit(self, X, y, sample_weight=None)

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

Parameters

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
y (array-like of shape (n_samples,)) – The target values (class labels).
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights. If None, the sample weights are initialized to 1 / n_samples.

Returns

self – Fitted estimator.

Return type

object

predict(self, X)

Predict classes for X.

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

Parameters: X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns: y – The predicted classes.
Return type: ndarray of shape (n_samples,)

get_interpretation(self, directory, base_file_name, y_test, feature_ids, multi_class=False): Base method that returns an empty string if there is not interpretation method in the classifier’s module