Comments (1)
You can use this library in the same way that you use gridsearch. It is meant to optimize hyperparameters. Maybe you can include different feature selections as an hyperparameter and evolve it?
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Related Issues (20)
- Minimizing a loss function HOT 3
- Running Search with n_jobs>1 throws Deap Attribute Error. Official sklearn example HOT 2
- Enabling early-stopping in cv with proper eval_set HOT 1
- scores for individuals in score_results are wrong
- Add return_train_score flag HOT 2
- Can't instantiate abstract class EvolutionaryAlgorithmSearchCV with abstract methods _run_search HOT 6
- AttributeError: Can't get attribute 'Individual' on HOT 6
- stuck after gen 1... HOT 1
- NSGA and SPEA2 HOT 1
- Tests no longer pass with newest sklearn HOT 2
- Does not work with pipelines HOT 4
- Can you please add callback into EvolutionaryAlgorithmSearchCV().fit()? HOT 2
- ValueError when calling cv.fit() for optimising a neural network
- njobs > 1 throws error AttributeError: Can't get attribute 'Individual' on <module 'deap.creator' from '\\site-packages\\deap\\creator.py'> HOT 1
- Problem with scikit 0.24 HOT 5
- .score method throws up error: "AttributeError: 'EvolutionaryAlgorithmSearchCV' object has no attribute 'multimetric_'" HOT 1
- 这个模型能否为回归模型进行调整参数呢? HOT 1
- It would be nice if there was a random_state parameter
- cannot install evolutionary_search and doesnot present on any source --see the comment HOT 2
- Not compatible with scikit-learn 1.1 and above HOT 1
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