Lately, I was creating content about Support Vector Machines (SVM) . The reason it was on my radar is that I saw a recent Kaggle competition where one of the top candidates used SVM, alongside XGBoost , CatBoost , and Neural Networks . I had completely forgotten about algorithms like Naive Bayes and SVM. What do they really represent when applied to the Titanic dataset we usually analyze? Anyway, I wanted to put SVM into perspective, to see how it handles non-linear data. Is tuning even suited to an algorithm like this? I did some research, & oh boy! There’s a lot of traditional machine learning I need to remember, or should I say, “re-learn”, from an updated perspective. Kernel trick methods that fit high-dimensional, complex data. Soft margin vs. hard margin, how they balance model performance. Did you know SVM can handle novelty detection and anomaly detection? I didn’t! Anyway, I ended up opening my journal and writing down every important idea I learned, then created some slides in Canva to put what I’ve learned into perspective. Tell me have you seen situations where certain algorithms are underestimated despite having great potential? PS: I wanted to share the PDF, but it only accepts images. Would you be interested if I uploaded it to Google Drive and shared it with you instead? Support Vector Machines (SVM) submitted by /u/The_Simpsons_22
Originally posted by u/The_Simpsons_22 on r/ArtificialInteligence