๐จ๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ๐ถ๐ป๐ด ๐๐ต๐ฒ ๐๐ฒ๐ ๐๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฒ๐๐๐ฒ๐ฒ๐ป ๐๐ถ๐ป๐ฒ๐ฎ๐ฟ ๐ฎ๐ป๐ฑ ๐๐ผ๐ด๐ถ๐๐๐ถ๐ฐ ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป
Grasping the fundamental distinction between linear and logistic regression is crucial for anyone diving into machine learning. Hereโs a brief breakdown:
๐๐ถ๐ป๐ฒ๐ฎ๐ฟ ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป: The objective is to find the best-fit line that ๐บ๐ถ๐ป๐ถ๐บ๐ถ๐๐ฒ๐ the sum of distances between all data points and the line.
๐๐ผ๐ด๐ถ๐๐๐ถ๐ฐ ๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป: The focus shifts to finding a hyperplane that ๐บ๐ฎ๐ ๐ถ๐บ๐ถ๐๐ฒ๐ the distance between distinct classes.
Another key difference lies in how distances are measured:
In ๐น๐ถ๐ป๐ฒ๐ฎ๐ฟ ๐ฟ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป, the distance is calculated between the predicted and actual points.
In ๐น๐ผ๐ด๐ถ๐๐๐ถ๐ฐ ๐ฟ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป, the perpendicular distance is calculated between the point and the separation line.
For a deeper dive into this topic, check out the Machine Learning Playlist Iโve curated: https://youtube.com/playlist?list=PLPTV0NXA_ZSibXLvOTmEGpUO6sjKS5vb-&si=4eKlS0IZgxSPcewb by Pritam Kudale
Additionally, Iโve made the ๐ฐ๐ผ๐ฑ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ต๐ถ๐ ๐ฎ๐ป๐ถ๐บ๐ฎ๐๐ถ๐ผ๐ป publicly availableโfeel free to explore and experiment. https://github.com/pritkudale/Code_for_LinkedIn/blob/main/Linear_vs_Logistic_Regression_Animation.ipynb
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