Return models used in adaboost python

Question

After applying adaboost on svm, I want to know the models (their parameters) used in the adaboost algorithm.

ada=AdaBoostClassifier(n_estimators=10, base_estimator=SVC(probability=True)) 
ada.fit(x_train,y_train)

How can I find the models used in adaboost.Thank you

Answer 1

estimators_Your object field AdaBoostClassifiercontains each of your models. Viewing the details of these models will depend on what was used to create them. For example, you may need to see how to search for information about DecisionTreeClassifierin the following example:

>>> 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')