ml-fundamentals

Preventing data leakage in sklearn pipelines requires fitting transformers only on training data and applying transformations to test sets. This Skill guides you through proper pipeline construction and OOP patterns to maintain strict separation between training and validation data.

The best way to structure an ML project for reproducibility is using Cookiecutter Data Science. This Skill provides guidelines to organize your code, data, and scripts systematically, ensuring experiments remain maintainable and reproducible.

Yes, familiarity with object-oriented programming is needed to use sklearn pipelines effectively. This Skill requires knowledge of OOP patterns and Python data manipulation to implement robust data handling and model training workflows.

Cookiecutter Data Science provides a standardized directory layout that improves reproducibility compared to manual structuring. This Skill applies its template to enforce best practices in ML project organization, avoiding common pitfalls like disorganized scripts and data paths.

Overfitting during refactoring often happens when data transformations leak test information into training. This Skill helps you restructure code using sklearn pipelines and OOP patterns to isolate validation data and prevent overfitting.