Machine learning models (LR, LDA, KNN, CART, NB, SVM) (by J. Brownlee, https://machinelearningmastery.com/machine-learning-in-python-step-by-step/).
Logistic regression (LR) : Estimates a multiple linear regression function.
Linear Discriminant Analysis (LDA) : Estimates the mean and variance from the dataset’s features for each class and uses Bayes Theorem to estimate the probability of observations belonging to classes (assumes Gaussian class-conditional density models).
k-Nearest Neighbors (k-NN) : Assigns class to observations based on the selected value k of its nearest neighbors in the feature space.
Classification and regression trees (CART) : Iterates across features and finds rules which best divide data into given classes along a tree structure.
Naive Bayes (NB) : Uses Bays Theorem to estimate the probability of observations belonging to classes (assumes features to be independent).
Support-Vector Machine (SVM) : Observations are mapped into progressively higher dimensions until a hyperplane is found which best divides the dataset into classes (support vectors are the observations nearest to the hyperplane).