CODEWITHSUNDEEP
pythonmachine-learningscikit-learnsvmadaboost
beaver
beaver

Reputation: 570

Why does AdaBoostClassifier with SVM work worse

By working worse, I mean even a higher training error. 

# Boosted SVC
clf = AdaBoostClassifier(base_estimator=SVC(random_state=1), random_state=1, algorithm="SAMME", n_estimators=5)
clf.fit(X, y)

# Only SVC
clf = SVC()
clf.fit(X, y)

My training data is

enter image description here

The result of boosted SVM: enter image description here

And the result of SVM:

enter image description here

Upvotes: 2

Views: 1593

Answers (1)

Nikos Epitropakis
Nikos Epitropakis

Reputation: 71

The main concept of adaBoost is to combine weak learners, thats why the default classifier is a decision stump. So by using SVM (strong classifier) as a weak one, you are loosing the concept of ensemble learning and you are getting worst results.

Upvotes: 2

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