Binary Classification

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In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

All existing training algorithms presented in this section are designed to solve binary classification tasks:

Linear SVM (Support Vector Machines)
Decision Trees
Multilayer perceptron
Logistic Regression
k-NN Classification
ANN (Approximate Nearest Neighbor)
Naive Bayes

Binary or binomial classification is the task of classifying the elements of a given set into two groups (predicting which group each one belongs to) on the basis of a classification rule.