User seeks efficient hyperparameter tuning for large cell classification dataset

A user on r/MachineLearning is seeking advice on efficient hyperparameter tuning for a large dataset of 4.3 million cells with 512 features. The dataset is imbalanced, and the user wants to implement a contextual bandit to augment training, but standard hyperparameter tuning methods are too time-consuming, even with subsampling. They are exploring alternatives to Optuna and looking for literature or similar experiences to address this bottleneck. AI

IMPACT This query highlights a practical challenge in applying machine learning to large datasets, specifically concerning computational efficiency in hyperparameter tuning.

RANK_REASON User is asking a question about a technical challenge in machine learning, not announcing a new development.

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