Bayesian Target Encoding to boost model accuracy - Clearly Explained

Welcome to the eighteenth video of the series "Build your First Machine Learning Project". In this we'll see Bayesian Target Encoding.

Bayesian Encoding is a type of encoding that takes into account intra-category variation and the target mean when encoding categorical variables.

Let's understand it in deep.

Chapters

00:00 Intro to Bayesian Target encoding
05:19 Approaches to overcome problems
05:53 First Method
08:40 Second method
12:31 Third Method
14:11 Fourth method
20:06 Mean encoding
22:36 Fifth Method
24:16 Implementing the method
27:46 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: https://www.youtube.com/watch?v=KSsjPbowHQ0&list=PLFAYD0dt5xCymcvacfR4CLB9Pk_9L50gz

Checkout Complete Machine Learning Plus Self-Paced Online Courses here:
https://edu.machinelearningplus.com/s/pages/ds-career-path

Previous Lesson:
Target Encoding in ML: https://www.youtube.com/watch?v=hGAbsHWitwo

Earlier Lessons:
1. Build your first ML Project: https://youtu.be/KSsjPbowHQ0
2. How to Formulate ML Problem: https://youtu.be/ygayqatDEDk
3. Setup Python Environment: https://youtu.be/Yk9BFMO6QXE
4. Jupyter Notebook Tutorial: https://youtu.be/4yuo96HtTw8
5. What is ML Modeling: https://youtu.be/Bcfk4HKgC5E
6. Reduce the size of Pandas Dataframe: https://youtu.be/Xa26NB75htg
7. What is EDA: https://youtu.be/rqCZZBrfNak
8. How to impute missing Data: https://youtu.be/Qir0Qi_CD2o
9. Mice Imputation Algorithm: https://youtu.be/BjyUbk258o4
10. How to impute missing data in categorical Variables: https://youtu.be/dm7YNsN_Nwo
11. How to Detect Outliers with Z Score: https://youtu.be/Qv2vCviL4iU
12. Mahalanobis distance: https://youtu.be/YY1lz8cOgH8
13. Cook's Distance: https://youtu.be/PVJYdMjfrAA
14. Isolation Forest: https://youtu.be/kqAxfOPlr1U
15. Feature Encoding: -----Link to be updated after publishing

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