Evaluation of binary classifiersThe evaluation of binary classifiers compares two methods of assigning a binary attribute, one of which is usually a standard method and the other is being investigated. There are many metrics that can be used to measure the performance of a classifier or predictor; different fields have different preferences for specific metrics due to different goals. For example, in medicine sensitivity and specificity are often used, while in computer science precision and recall are preferred.
Sensitivity analysisSensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should be run in tandem.
Distance matrixIn mathematics, computer science and especially graph theory, a distance matrix is a square matrix (two-dimensional array) containing the distances, taken pairwise, between the elements of a set. Depending upon the application involved, the distance being used to define this matrix may or may not be a metric. If there are N elements, this matrix will have size N×N. In graph-theoretic applications, the elements are more often referred to as points, nodes or vertices. In general, a distance matrix is a weighted adjacency matrix of some graph.
MulticollinearityIn statistics, multicollinearity (also collinearity) is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy. In this situation, the coefficient estimates of the multiple regression may change erratically in response to small changes in the model or the data. Multicollinearity does not reduce the predictive power or reliability of the model as a whole, at least within the sample data set; it only affects calculations regarding individual predictors.
Capital assetA capital asset is defined as property of any kind held by an assessee, whether connected with their business or profession or not connected with their business or profession. It includes all kinds of property, movable or immovable, tangible or intangible, fixed or circulating. Thus, land and building, plant and machinery, motorcar, furniture, jewellery, route permits, goodwill, tenancy rights, patents, trademarks, shares, debentures, securities, units, mutual funds, zero-coupon bonds etc. are capital assets.
