Search results
Results from the WOW.Com Content Network
Median of medians. In computer science, the median of medians is an approximate median selection algorithm, frequently used to supply a good pivot for an exact selection algorithm, most commonly quickselect, that selects the k th smallest element of an initially unsorted array. Median of medians finds an approximate median in linear time.
Selection algorithm. In computer science, a selection algorithm is an algorithm for finding the th smallest value in a collection of ordered values, such as numbers. The value that it finds is called the th order statistic. Selection includes as special cases the problems of finding the minimum, median, and maximum element in the collection.
Mode (statistics) In statistics, the mode is the value that appears most often in a set of data values. [1] If X is a discrete random variable, the mode is the value x at which the probability mass function takes its maximum value (i.e., x=argmaxxi P (X = xi)). In other words, it is the value that is most likely to be sampled.
This threshold is determined by minimizing intra-class intensity variance, or equivalently, by maximizing inter-class variance. [2] Otsu's method is a one-dimensional discrete analogue of Fisher's Discriminant Analysis , is related to Jenks optimization method , and is equivalent to a globally optimal k-means [ 3 ] performed on the intensity ...
The re-sampling techniques are implemented in four different categories: undersampling the majority class, oversampling the minority class, combining over and under sampling, and ensembling sampling. The Python implementation of 85 minority oversampling techniques with model selection functions are available in the smote-variants [2] package.
Median. Finding the median in sets of data with an odd and even number of values. The median of a set of numbers is the value separating the higher half from the lower half of a data sample, a population, or a probability distribution. For a data set, it may be thought of as the “middle" value.
Location–scale family. In probability theory, especially in mathematical statistics, a location–scale family is a family of probability distributions parametrized by a location parameter and a non-negative scale parameter. For any random variable whose probability distribution function belongs to such a family, the distribution function of ...
Local regression or local polynomial regression, [1] also known as moving regression, [2] is a generalization of the moving average and polynomial regression. [3] Its most common methods, initially developed for scatterplot smoothing, are LOESS (locally estimated scatterplot smoothing) and LOWESS (locally weighted scatterplot smoothing), both pronounced / ˈ l oʊ ɛ s / LOH-ess.