𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗞𝗲𝘆 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗟𝗶𝗻𝗲𝗮𝗿 𝗮𝗻𝗱 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻

Linear vs Logistic Regression

Grasping the fundamental distinction between linear and logistic regression is crucial for anyone diving into machine learning. Here’s a brief breakdown:

𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: The objective is to find the best-fit line that 𝗺𝗶𝗻𝗶𝗺𝗶𝘇𝗲𝘀 the sum of distances between all data points and the line.

𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: The focus shifts to finding a hyperplane that 𝗺𝗮𝘅𝗶𝗺𝗶𝘇𝗲𝘀 the distance between distinct classes.

Another key difference lies in how distances are measured:

In 𝗹𝗶𝗻𝗲𝗮𝗿 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻, the distance is calculated between the predicted and actual points.

In 𝗹𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻, 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 I’ve curated: https://youtube.com/playlist?list=PLPTV0NXA_ZSibXLvOTmEGpUO6sjKS5vb-&si=4eKlS0IZgxSPcewb by Pritam Kudale

Additionally, I’ve made the 𝗰𝗼𝗱𝗲 𝗳𝗼𝗿 𝘁𝗵𝗶𝘀 𝗮𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻 publicly available—feel free to explore and experiment. https://github.com/pritkudale/Code_for_LinkedIn/blob/main/Linear_vs_Logistic_Regression_Animation.ipynb

Stay updated with more such engaging content by subscribing to 𝗩𝗶𝘇𝘂𝗮𝗿𝗮’𝘀 𝗔𝗜 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://www.vizuaranewsletter.com?r=502twn

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