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