Scikit-learn Best Practices

Expert guidelines for scikit-learn development, focusing on machine learning workflows, model development, evaluation, and best practices.

Code Style and Structure

• Write concise, technical responses with accurate Python examples
• Prioritize reproducibility in machine learning workflows
• Use functional programming for data pipelines
• Use object-oriented programming for custom estimators
• Prefer vectorized operations over explicit loops
• Follow PEP 8 style guidelines

Machine Learning Workflow

Data Preparation

• Always split data before any preprocessing: train/validation/test
• Use train_test_split() with random_state for reproducibility