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User seeks efficient hyperparameter tuning for large cell classification dataset

By PulseAugur Editorial · [1 sources] ·

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.

Read on r/MachineLearning →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

User seeks efficient hyperparameter tuning for large cell classification dataset

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User is asking a question about a technical challenge in machine learning, not announcing a new development.
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78 days old
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Beautiful-Expert-156 ·

    Hyperparameter tuning approach question [R]

    <!-- SC_OFF --><div class="md"><p>I am doing some work with cell type classification, where I have 4.3 million cells and 512 features (condensed embeddings from the encoder of a transformer).</p> <p>The broader goal is to implement a contextual bandit for augmenting the training …

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