The estimators_
field of your AdaBoostClassifier
object contains each of your models. Viewing the details of those models will depend on what was used to build them. So for example you may need to view how to lookup how to get information for a DecisionTreeClassifier
in the example below:
>>> from sklearn.datasets import load_iris
>>> from sklearn.ensemble import AdaBoostClassifier
>>>
>>> iris = load_iris()
>>> clf = AdaBoostClassifier(n_estimators=2)
>>> clf.fit(iris.data, iris.target)
AdaBoostClassifier(algorithm='SAMME.R',
base_estimator=DecisionTreeClassifier(compute_importances=None, criterion='gini',
max_depth=1, max_features=None, min_density=None,
min_samples_leaf=1, min_samples_split=2, random_state=None,
splitter='best'),
learning_rate=1.0, n_estimators=2, random_state=None)
>>> clf.estimators_
[DecisionTreeClassifier(compute_importances=None, criterion='gini',
max_depth=1, max_features=None, min_density=None,
min_samples_leaf=1, min_samples_split=2, random_state=None,
splitter='best'), DecisionTreeClassifier(compute_importances=None, criterion='gini',
max_depth=1, max_features=None, min_density=None,
min_samples_leaf=1, min_samples_split=2, random_state=None,
splitter='best')]
>>>
>>> #first model
... clf.estimators_[0]
DecisionTreeClassifier(compute_importances=None, criterion='gini',
max_depth=1, max_features=None, min_density=None,
min_samples_leaf=1, min_samples_split=2, random_state=None,
splitter='best')
>>> #second model
... clf.estimators_[1]
DecisionTreeClassifier(compute_importances=None, criterion='gini',
max_depth=1, max_features=None, min_density=None,
min_samples_leaf=1, min_samples_split=2, random_state=None,
splitter='best')