Understanding Target Encoding for Categorical Features
Welcome to the seventeenth video of the series "Build your First Machine Learning Project". In this we'll see Target Encoding aka Mean Encoding.
Target encoding is the process of replacing a categorical value with the mean of the target variable. Any non-categorical columns are automatically dropped by the target encoder model.
Let's understand it in deep.
Chapters
0:00 Intro to Target Encoding
1:12 Only two possible values
5:11 More than 2 categories
8:24 Numeric Data problem
11:30 Data Leakage
13:50 Conclusion
In order to make the best out of this, please watch this series in the order in playlist: Build Your First ML Model Playlist: • Build Your FIRST Machine Learning Project ...
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Previous Lesson:
Feature Encoding in ML: --- • Feature Encoding in ML: Beyond the Basics ----
Earlier Lessons:
1. Build your first ML Project: • Build Your FIRST Machine Learning Project ...
2. How to Formulate ML Problem: • Build Your First ML Project part 2: How t...
3. Setup Python Environment: • Setup Python Environment using ANACONDA
4. Jupyter Notebook Tutorial: • Jupyter Notebook Tutorial - How to Install...
5. What is ML Modeling: • What is ML Modeling? (Problem statement an...
6. Reduce the size of Pandas Dataframe: • Reduce the memory size of Pandas Dataframe...
7. What is EDA: • Exploratory Data Analysis (EDA) - Use thes...
8. How to impute missing Data: • How to handle missing data for machine lea...
9. Mice Imputation Algorithm: • Multiple Imputation by Chained Equations (...
10. How to impute missing data in categorical Variables: • How to impute missing data in categorical ...
11. How to Detect Outliers with Z Score: • How to Detect Outliers with Z Score | Clea...
12. Mahalanobis distance: • Why mahalanobis distance is incredibly pow...
13. Cook's Distance: • Understanding Cooks Distance to detect inf...
14. Isolation Forest: • Isolation Forest: A Tree based approach fo...
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