Confusion matrixA confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of a person or an algorithm on a specific task. In audiology these matrices measure how a person can hear specific words or sounds. In machine learning these matrices show the success of the learning system both in supervised learning and unsupervised learning, where they are usually called matching matrix.
Binary classificationBinary classification is the task of putting things into one of two categories (each called a class). As such, it is the simplest form of the general task of classification into any number of classes.
Multiclass classificationIn machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification).
DiseaseA disease is a particular abnormal condition that adversely affects the structure or function of all or part of an organism and is not immediately due to any external injury. Diseases are often known to be medical conditions that are associated with specific signs and symptoms. A disease may be caused by external factors such as pathogens or by internal dysfunctions.
Logistic regressionStatistical model for a binary dependent variableExample graph of a logistic regression curve fitted to data. The curve shows the estimated probability of passing an exam (binary dependent variable) versus hours studying (scalar independent variable). See § Example for worked details.In statistics, a logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables.Statistical classificationWhen classification is performed by a computer, statistical methods are normally used to develop the algorithm. Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.Type I and type II errorsType I error, or a false positive, is the incorrect rejection of a true null hypothesis in statistical hypothesis testing. A type II error, or a false negative, is the incorrect failure to reject a false null hypothesis. An analysis commits a Type I error when some baseline assumption is incorrectly rejected because of new, misleading information.
Medical testA medical test is a medical procedure performed to detect, diagnose, or monitor diseases, disease processes, susceptibility, or to determine a course of treatment. Medical tests such as, physical and visual exams, diagnostic imaging, genetic testing, chemical and cellular analysis, relating to clinical chemistry and molecular diagnostics, are typically performed in a medical setting.
Bayes classifierIn statistical classification, the Bayes classifier is the classifier having the smallest probability of misclassification of all classes using the same set of features. DefinitionSuppose a pair (X,Y){\displaystyle (X,Y)} takes values in Rd×{1,2,…,K}{\displaystyle \mathbb {R} ^{d}\times \{1,2,\dots ,K\}}, where Y{\displaystyle Y} is the class label of an element whose features are given by X{\displaystyle X}.
Class (set theory)In set theory and its applications throughout mathematics, a class is a collection of mathematical objects (often sets) that can be unambiguously defined by a property that all its members share. Classes act as a way to have set-like collections while differing from sets so as to avoid paradoxes, especially Russell's paradox (see § Paradoxes). The precise definition of "class" depends on foundational context.
Probit modelIn statistics, a probit model is a type of regression where the dependent variable can take only two values, for example married or not married. The word is a portmanteau, coming from probability + unit. The purpose of the model is to estimate the probability that an observation with particular characteristics will fall into a specific one of the categories; moreover, classifying observations based on their predicted probabilities is a type of binary classification model.*As an Amazon Associate I earn from qualifying purchases.