Understanding Cooks Distance to detect influential observations

Welcome to the fourteenth video of the series "Build your First Machine Learning Project". In this, we'll see cook's distance for outlier detection.
Notebook Link: github.com/machinelearningplu...
Cook’s distance, is used in Regression Analysis to find influential outliers in a set of predictor variables. In other words, it’s a way to identify points that largely impacts the observation values / predictions.
The measurement is a combination of each observation’s leverage and residual values; the higher the leverage and residuals, the higher the Cook’s distance.
So let's understand it in brief.
Chapters
0:00 Intro to Cooks Distance
1:58 First step
4:15 Second Step
9:22 Implementing in Python
14:58 Conclusion
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Previous Lesson:
Why mahalanobis distance is incredibly powerful for outlier detection : • Why mahalanobis distan...
Earlier Lessons:
1. Build your first ML Project: • Build Your FIRST Machi...
2. How to Formulate ML Problem: • Build Your First ML Pr...
3. Setup Python Environment: • Setup Python Environme...
4. Jupyter Notebook Tutorial: • Jupyter Notebook Tutor...
5. What is ML Modeling: • What is ML Modeling? (...
6. Reduce the size of Pandas Dataframe: • Reduce the memory size...
7. What is EDA: • Exploratory Data Analy...
8. How to impute missing Data: • How to handle missing ...
9. Mice Imputation Algorithm: • Multiple Imputation by...
10. How to impute missing data in categorical Variables: • How to impute missing ...
11. How to Detect Outliers with Z Score: • How to Detect Outliers...
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[@kaido453]

Amazing video! thank you

[@machinelearningplus]

Thank you :)

[@jamalnuman]

Great. How the Cook's D can be computed within the GWR?