CODEWITHSUNDEEP
scikit-learnxgboostcross-validationhyperparametersmulticlass-classification
Leon J
Leon J

Reputation: 41

OneVsRestClassifier Hyperparameter Tuning for every base estimator

I am working on a multiclass problem with six different classes and I am using OneVsRestClassifier. I have then performed hyperparameter tuning with GridSearchCV and obtained the optimized classifier with clf.best_estimator_.

As far as I understand, this returns one set of the hyperparameters for the aggregated model/every base estimator. Is there a way to perform hyperparameter tuning separately for each base estimator?

Upvotes: 0

Views: 517

Answers (1)

Ben Reiniger
Ben Reiniger

Reputation: 12602

Sure, just reverse the order of the search and the multiclass wrapper:

one_class_clf = GridSearchCV(base_classifier, params, ...)
clf = OneVsRestClassifier(one_class_clf)

Fitting clf generates the one-vs-rest problems, and for each of those fits a copy of the grid-searched base_classifier.

Upvotes: 2

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