summit.multiview_platform.monoview_classifiers.adaboost
adaboost
- classifier_class_name = 'Adaboost'
- class Adaboost(random_state=None, n_estimators=50, estimator=None, estimator_config=None, **kwargs)
This class is an adaptation of scikit-learn’s AdaBoostClassifier
- param_names = ['n_estimators', 'estimator']
- classed_params = ['estimator']
- distribs
- weird_strings
- plotted_metric
- plotted_metric_name = 'zero_one_loss'
- step_predictions = None
- estimator_config = None
- fit(X, y, sample_weight=None)
Build a boosted classifier/regressor 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.
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(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(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