Hi everyone I’m sharing Week Bites , a series of light, digestible videos on data science . Each week, I cover key concepts, practical techniques, and industry insights in short, easy-to-watch videos. Before You Touch XGBoost: Why Random Forest Is Your Best Starting Point Despite Random forest is a black-box algorithm, unlike logistic regression where you can what features impact the predictions and you able to modify the threshold. Random Forest lean to feature importance and SHAP for that. Random Forest is insensitive about mislabeled values and it isn’t prone to overfitting as decision tree. Built-in Interpretability: Why Decision Trees Don’t Need SHAP Decision Tree is a versatile algorithm with its Entropy and Gini impurity and information gain features, the downside is that it’s prone to overfitting. To encounter such a problem, we engineer the “max_depth” attribute or prune the splitting nodes “backward” to reduce the overfitting. The “Kernel Trick” Explained: How SVMs Handle Non-Linear Data Support Vector Machines can feel like a black box at first, but once you get the intuition behind it, it just click! My purpose is to cover when to use it (and when NOT to), the kernel trick explained simply (Linear, Polynomial, RBF, Sigmoid), how Regularization © and Gamma control your decision boundary, Soft Margin vs. Hard Margin, and I wrap up with the exact interview questions you’ll likely get asked about SVM. Would love to hear your thoughts, feedback, and topic suggestions ! Let me know which topics you find most useful submitted by /u/The_Simpsons_22
Originally posted by u/The_Simpsons_22 on r/ArtificialInteligence
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