06 Leave-One-Out encoding (Categorical encoding Python Machine Learning AI Data preprocessing)

In this video, we delve into the world of categorical variable encoding, focusing on the Leave-One-Out technique. Whether you're a beginner in data science or looking to expand your understanding of encoding methods, this video will provide you with a comprehensive guide to Leave-One-Out encoding. We'll break down the key concepts and walk you through step-by-step Python implementations. Plus, you'll discover how to apply Leave-One-Out to your datasets and understand when it's the best method for your machine learning workflows.
Check out other videos in our series, covering 37 different encoding methods, grouped into 8 categories, and learn how to choose the right encoding technique with a comparative analysis using standard datasets.
What you'll learn in this video:
• Detailed explanation of Leave-One-Out encoding
• Python code implementations
• When and why to use Target Mean Encoding with k-fold Cross-validation for your machine learning models
This video is part of a larger series on advanced categorical encoding methods. Subscribe and turn on notifications to stay updated as we release more tutorials on all 38 encoding techniques!
Methods covered in this series:
1. Basic Encoding Techniques (Simple transformation of categories into numbers)
• One-hot encoding
• Label encoding
• Ordinal encoding
2. Target-Based Encoding (Encoding based on relationship with the target variable)
• Target Encoding (Mean Encoding)
• Target Mean Encoding with k-fold Cross-validation
• Leave-One-Out Encoding
• Bayesian Encoding
• James-Stein Encoding
• M-estimator Encoding
• Smooth Target Encoding
• Probability Ratio Encoding
3. Frequency or Count Based Encoding (Using counts or frequency of categories)
• Frequency Encoding
• Count Encoding
4. Binary and Hash Encoding Methods (Represent categories in binary or hash form)
• Binary Encoding
• Hash Encoding
• Geohash Encoding
• Gray Encoding
• BaseN Encoding
5. Mathematical or Statistical Encoding (Using mathematical or statistical models)
• Effect Encoding (Deviation Encoding)
• Backward Difference Encoding
• Polynomial Encoding
• Generalized Linear Mixed Models (GLMM)
• Kernel Feature Maps
• Principal Component Encoding (PCA-based encoding)
• Regularized Encoding
• Weight of Evidence (WOE) Encoding
• CatBoost Encoding
6. Decision Tree-Based Encoding (Using decision trees for encoding)
• Decision Tree Encoding
7. Encoding for Special Scenarios (Handling special data types or cases)
• Thermometer Encoding
• Rank Hot Encoding
• Sum Encoding
• Quantile Encoding
• Similarity Encoding
• Time-based Encoding
• Rare Category Encoding
• Entity Embedding
8. Advanced Encoding (More complex or rarely used methods)
• Facet Encoding
• Difference Encoding (Helmert Encoding)

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