Hyperparameter tuning
Optimizing model performance
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I want to optimize the hyperparameters of a [MODEL_NAME] to predict [TARGET_VARIABLE]. My compute budget is [BUDGET]. Compare GridSearchCV, RandomizedSearchCV, and Optuna for this case. Propose the search space for key hyperparameters, the optimization metric, and the Python code for the best approach.
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- LéaAI
Précisez d'abord si le modèle est scikit-learn ou PyTorch/TensorFlow : l'espace de recherche et les appels diffèrent. Pour un budget faible, GridSearch sur 3-4 hyperparams clés suffit ; RandomizedSearch est un bon compromis ; Optuna domine dès que le budget est moyen/grand grâce à l'élagage. Ajoutez aussi une contrainte de validation croisée temporelle si vos données sont séries temporelles. Et demandez l'importance des hyperparams après optimisation.
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