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02 Label encoding (Categorical variable encoding - Python code Machine Learning AI)
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In this video, we dive deep into the world of categorical data encoding, focusing on the Basic Encoding Techniques of Label encoding. Whether you're new to data science or looking to solidify your understanding, this video will guide you through step-by-step explanations and key concepts. You may want to check out other videos containing 37 other encoding methods, grouped into 8 comprehensive categories, a complete cheat-sheet on how to choose right method and comparative analysis using standard datasets.
Learn how each method works, the advantages and limitations of one-hot encoding. Enhance your knowledge and apply these techniques in your data preprocessing workflows for better performance in machine learning models.
What you'll learn in this video:
• In-depth explanation of label encoding
• Python code implementations for label encoding using one more of below libraries:
o Custom logic using Pandas and Numpy
o sklearn library
o category_encoder library
o feature_engine library
o
• When and why to choose label encoding method
This video is part of a larger series covering advanced categorical encoding methods. Subscribe to stay updated as we release detailed tutorials for all 38 encoding methods!
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)
Keywords: categorical encoding, label, data preprocessing, machine learning, data science, categorical variables, feature engineering, encoding techniques, python coding, basic encoding, data transformation, data handling, machine learning models, supervised learning, real-world examples, encoding in python, data processing, AI, machine learning tutorial, data science beginner, ordinal data, nominal data, python tutorial, encoding methods comparison, feature encoding, data science series
Learn how each method works, the advantages and limitations of one-hot encoding. Enhance your knowledge and apply these techniques in your data preprocessing workflows for better performance in machine learning models.
What you'll learn in this video:
• In-depth explanation of label encoding
• Python code implementations for label encoding using one more of below libraries:
o Custom logic using Pandas and Numpy
o sklearn library
o category_encoder library
o feature_engine library
o
• When and why to choose label encoding method
This video is part of a larger series covering advanced categorical encoding methods. Subscribe to stay updated as we release detailed tutorials for all 38 encoding methods!
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)
Keywords: categorical encoding, label, data preprocessing, machine learning, data science, categorical variables, feature engineering, encoding techniques, python coding, basic encoding, data transformation, data handling, machine learning models, supervised learning, real-world examples, encoding in python, data processing, AI, machine learning tutorial, data science beginner, ordinal data, nominal data, python tutorial, encoding methods comparison, feature encoding, data science series
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