09 M-estimator encoding (Categorical encoding Python Machine Learning AI Data preprocessing)

In this video, we delve into the world of categorical variable encoding, focusing on the M-estimator encoding 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 M-estimator encoding. We'll break down the key concepts and walk you through step-by-step Python implementations. Plus, you'll discover how to apply M-estimator encoding to your datasets and understand when it's the best method for your machine learning workflows.
Check out other videos in our series, covering 38 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 M-estimator encoding
• Python code implementations
• When and why to use M-estimator encoding 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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