Your First Machine Learning Project - Crash Course - 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: https://github.com/machinelearningplus/Build-Your-First-ML-Project/blob/main/13c_Cooks%20Distance/Cooks%20Distance%20-%20How%20to%20detect%20multivariate%20outliers.ipynb 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: https://www.youtube.com/watch?v=KSsjPbowHQ0&list=PLFAYD0dt5xCymcvacfR4CLB9Pk_9L50gz Previous Lesson: Why mahalanobis distance is incredibly powerful for outlier detection : https://youtu.be/YY1lz8cOgH8 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 Let me know in the comments section if you have any questions! If you enjoyed this video, be sure to throw it a like and make sure to subscribe to not miss any future videos! Thanks for watching! #mlmodeling, #python, #machinelearning, #artificialintelligence, #pandas, #datascience