AdaBoostClassifier with different base learners

I am trying to use AdaBoostClassifier with a base learner other than DecisionTree. I have tried SVM and KNeighborsClassifier but I get errors. Can some one point out the classifiers that can be used with AdaBoostClassifier?

Ok, we have a systematic method to find out all the base learners supported by AdaBoostClassifier. Compatible base learner's fit method needs to support sample_weight, which can be obtained by running following code:

import inspect
from sklearn.utils.testing import all_estimators
for name, clf in all_estimators(type_filter='classifier'):
    if 'sample_weight' in inspect.getargspec(clf().fit)[0]:
       print name

This results in following output: AdaBoostClassifier, BernoulliNB, DecisionTreeClassifier, ExtraTreeClassifier, ExtraTreesClassifier, MultinomialNB, NuSVC, Perceptron, RandomForestClassifier, RidgeClassifierCV, SGDClassifier, SVC.

If the classifier doesn't implement predict_proba, you will have to set AdaBoostClassifier parameter algorithm = 'SAMME'.

Thanks to Andreas for showing how to list all estimators.

You shouldnot use SVM with Adaboost. Adaboost should use weak-classifier. Using of classifiers like SVM will result in overfitting.

Any classifier that supports passing sample weights should work. SVC is one such classifier. What specific error message (and traceback) do you get? Can you provide a minimalistic reproduction case for this error (e.g. as a http://gist.github.com )?

来源：https://stackoverflow.com/questions/18306416/adaboostclassifier-with-different-base-learners

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machine-learning

scikit-learn

adaboost