Search results
Results from the WOW.Com Content Network
Bootstrapping can be interpreted in a Bayesian framework using a scheme that creates new data sets through reweighting the initial data. Given a set of data points, the weighting assigned to data point in a new data set is =, where is a low-to-high ordered list of uniformly distributed random numbers on [,], preceded by 0 and succeeded by 1.
A data set (or dataset) is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as for example ...
For example, it has been used to assess novel drugs, [37] models of clinical care, in audit of clinical services and in assessment of teledermatology. [36] The DLQI is the most widely used quality of life outcome measure in randomised controlled trials of therapies for psoriasis.
These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high ...
Winsorizing or winsorization is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers. It is named after the engineer-turned-biostatistician Charles P. Winsor (1895–1951). The effect is the same as clipping in signal processing. The distribution of many ...
Imputation (statistics) In statistics, imputation is the process of replacing missing data with substituted values. When substituting for a data point, it is known as " unit imputation "; when substituting for a component of a data point, it is known as " item imputation ". There are three main problems that missing data causes: missing data ...
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.
The Iris flower data set or Fisher's Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. [ 1 ] It is sometimes called Anderson's Iris data set because Edgar ...