Machine Learning, Decision Tree, Random Forest, Adaboost, Gradient Boosting.
hyperparameters SVM ✅ Performs well in higher dimensions ✧ Excellent when classes are separable ✧ Outliers have less impact ❌ Slow ✧ Poor performance with overlapping classes ✧ Selecting appropriate kernel functions can be tricky Naïve Bayes ✅ Fast ✧ Simple ✧ Requires less training data ✧ Scalable