Machine Learning, Logistic Regression, Support Vector Machine, Supervised Learning, Classification Algorithms.
n = number of features, m = number of training examples 1. If n is large (1–10,000) and m is small (10–1000) : use logistic regression or SVM with a linear kernel. 2. If n is small (1–10 00) and m is intermediate (10–10,000) : use SVM with (Gaussian, polynomial etc) kernel 3. If n is small (1–10