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
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 Machi...
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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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