BMAD-METHOD/.claude/rules/pytorch-scikit-learn-cursorrules-prompt-file/chemistry-ml---scikit-learn-models.mdc

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description: Guidelines for developing machine learning models using scikit-learn in chemistry applications, focusing on algorithm selection, hyperparameter tuning, and cross-validation.
globs: models/sklearn/**/*.py
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- Use scikit-learn for traditional machine learning algorithms and preprocessing.
- Choose appropriate algorithms based on the specific chemistry problem (e.g., regression, classification, clustering).
- Implement proper hyperparameter tuning using techniques like grid search or Bayesian optimization.
- Use cross-validation techniques suitable for chemical data (e.g., scaffold split for drug discovery tasks).
- Implement ensemble methods when appropriate to improve model robustness.