Median absolute deviationIn statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample. For a univariate data set X1, X2, ..., Xn, the MAD is defined as the median of the absolute deviations from the data's median : that is, starting with the residuals (deviations) from the data's median, the MAD is the median of their absolute values. Consider the data (1, 1, 2, 2, 4, 6, 9).
K-nearest neighbors algorithmIn statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression. In both cases, the input consists of the k closest training examples in a data set. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership.
Cataclysmic variable starIn astronomy, cataclysmic variable stars (CVs) are stars which irregularly increase in brightness by a large factor, then drop back down to a quiescent state. They were initially called novae (), since ones with an outburst brightness visible to the naked eye and an invisible quiescent brightness appeared as new stars in the sky. Cataclysmic variable stars are binary stars that consist of two components; a white dwarf primary, and a mass transferring secondary.
Color–color diagramA color–color diagram is a means of comparing the colors of an astronomical object at different wavelengths. Astronomers typically observe at narrow bands around certain wavelengths, and objects observed will have different brightnesses in each band. The difference in brightness between two bands is referred to as color. On color–color diagrams, the color defined by two wavelength bands is plotted on the horizontal axis, and the color defined by another brightness difference will be plotted on the vertical axis.
