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04 Target encoding mean encoding (Categorical variable encoding Python code Machine Learning AI)
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In this video, we explore Target Encoding (Mean Encoding), a powerful technique for encoding categorical data based on its relationship with the target variable. Whether you're a beginner in data science or looking to master advanced preprocessing methods, this video offers a step-by-step guide to Target Encoding, complete with detailed explanations and Python code examples. You’ll also discover when and why Target Encoding is useful, and how to apply it to improve machine learning model performance.
Check out other videos in this series covering 38 encoding methods, grouped into 8 categories, with comparative analysis and a cheat-sheet to help you choose the right method for your project.
What you'll learn in this video:
• Comprehensive overview of Target (Mean) Encoding
• Python code implementations using popular libraries:
o Custom code with Pandas
o feature_engine library
• Best practices for applying Target Encoding in real-world scenarios
• Pros and cons of using Target Encoding in machine learning
This tutorial is part of an ongoing series covering advanced encoding methods. Make sure to subscribe and stay updated as we release videos on all 38 encoding methods!
Methods covered in this series include:
1. Basic Encoding Techniques
2. Target-Based Encoding
3. Frequency or Count-Based Encoding
4. Binary and Hash Encoding Methods
5. Mathematical or Statistical Encoding
6. Decision Tree-Based Encoding
7. Encoding for Special Scenarios
8. Advanced Encoding Methods
Keywords: Target Encoding, Mean Encoding, categorical encoding, data preprocessing, machine learning, data science, categorical variables, feature engineering, encoding techniques, python coding, target-based encoding, data transformation, machine learning models, supervised learning, feature encoding, data science tutorial, advanced encoding methods, AI.
04 Target encoding mean encoding (Categorical variable encoding Python code Machine Learning AI)
Check out other videos in this series covering 38 encoding methods, grouped into 8 categories, with comparative analysis and a cheat-sheet to help you choose the right method for your project.
What you'll learn in this video:
• Comprehensive overview of Target (Mean) Encoding
• Python code implementations using popular libraries:
o Custom code with Pandas
o feature_engine library
• Best practices for applying Target Encoding in real-world scenarios
• Pros and cons of using Target Encoding in machine learning
This tutorial is part of an ongoing series covering advanced encoding methods. Make sure to subscribe and stay updated as we release videos on all 38 encoding methods!
Methods covered in this series include:
1. Basic Encoding Techniques
2. Target-Based Encoding
3. Frequency or Count-Based Encoding
4. Binary and Hash Encoding Methods
5. Mathematical or Statistical Encoding
6. Decision Tree-Based Encoding
7. Encoding for Special Scenarios
8. Advanced Encoding Methods
Keywords: Target Encoding, Mean Encoding, categorical encoding, data preprocessing, machine learning, data science, categorical variables, feature engineering, encoding techniques, python coding, target-based encoding, data transformation, machine learning models, supervised learning, feature encoding, data science tutorial, advanced encoding methods, AI.
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