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halvingrandomsearchcv

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Wizard-hash2 / Kaggle_Titanic_compettion

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I managed to participate in Kaggle Titanic competition where I manually code the model that can predict survival rate achieving 79% accuracy with SVM and systematic hyperparameter tuning: : feature engineering, optimization strategies : GridSearch, HalvingRandomSearch, RandomizedSearch

machine-learning kaggle-competition feature-engineering data-processing svm-classifier gridsearchcv randomizedsearchcv halvingrandomsearchcv
  • Updated Mar 14, 2026
  • Jupyter Notebook

rpcoelho17 / PatternSearch_HyperParam_Opt__V2

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scikit-learn-compatible hyperparameter search: BayesHalvingSearchCV (or HalvingBayesianSearchCV - Bayesian/GP) and PatternSearchCV (Hooke-Jeeves), with multi-fidelity data growth for faster tuning on large datasets

python machine-learning scikit-learn hyperparameter-optimization bayesian-optimization hyperparameter-tuning hyperparameter-search pattern-search scikit-learn-python model-optimizer hyperparameter-selection automl-algorithms model-optimization halvingrandomsearchcv halvingrandomizedsearchcv halvingbayesiansearchcv pattern-searchcv halvinggridsearchcv halving-gridsearchcv halving-randomsearchcv
  • Updated Aug 14, 2026
  • Jupyter Notebook

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