summit.multiview_platform.monoview_classifiers.imbalance_bagging
imbalance_bagging
- classifier_class_name = 'ImbalanceBagging'
- class ImbalanceBagging(random_state=None, estimator='DecisionTreeClassifier', n_estimators=10, sampling_strategy='auto', replacement=False, base_estimator_config=None)
Base class for all estimators in scikit-learn.
Inheriting from this class provides default implementations of:
setting and getting parameters used by GridSearchCV and friends;
textual and HTML representation displayed in terminals and IDEs;
estimator serialization;
parameters validation;
data validation;
feature names validation.
Read more in the User Guide.
Notes
All estimators should specify all the parameters that can be set at the class level in their
__init__
as explicit keyword arguments (no*args
or**kwargs
).Examples
>>> import numpy as np >>> from sklearn.base import BaseEstimator >>> class MyEstimator(BaseEstimator): ... def __init__(self, *, param=1): ... self.param = param ... def fit(self, X, y=None): ... self.is_fitted_ = True ... return self ... def predict(self, X): ... return np.full(shape=X.shape[0], fill_value=self.param) >>> estimator = MyEstimator(param=2) >>> estimator.get_params() {'param': 2} >>> X = np.array([[1, 2], [2, 3], [3, 4]]) >>> y = np.array([1, 0, 1]) >>> estimator.fit(X, y).predict(X) array([2, 2, 2]) >>> estimator.set_params(param=3).fit(X, y).predict(X) array([3, 3, 3])
- param_names = ['n_estimators', 'estimator', 'sampling_strategy']
- classed_params = ['estimator']
- distribs
- weird_strings
- base_estimator_config = None