Resampling and Regularization | Data Science with Marco

Data Science with Marco · Beginner ·📐 ML Fundamentals ·6y ago

Key Takeaways

The video covers resampling and regularization techniques in Python, including validation set, leave-one-out cross-validation, k-fold cross-validation, ridge regression, and lasso regularization.

Original Description

Get the notebook and the dataset: https://github.com/marcopeix/datasciencewithmarco 📚 Theory: 0:00 - 5:17 🐍 Code: 5:18 - 14:40 In this video, we cover resampling and regularization in Python. We cover 3 different approaches to resampling: validation set, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Then, we introduce two resampling methods: ridge regression and lasso. Finally, we apply all those methods in Python and see how they can improve our models. Follow me on Medium for data science articles: https://medium.com/@marcopeixeiro
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This video teaches resampling and regularization techniques in Python to improve model performance. It covers three resampling approaches and introduces two regularization methods, applying them in Python.

Key Takeaways
  1. Import necessary libraries
  2. Load dataset
  3. Split data into training and testing sets
  4. Apply validation set resampling
  5. Apply LOOCV resampling
  6. Apply k-fold cross-validation resampling
  7. Implement ridge regression
  8. Implement lasso regularization
  9. Compare model performance
💡 Resampling and regularization techniques can significantly improve model performance by reducing overfitting and bias.

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