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In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy).
The group of packages strives to provide a cohesive collection of functions to deal with common data science tasks, including data import, cleaning, transformation and visualisation (notably with the ggplot2 package). The R Infrastructure packages [31] support coding and the development of R packages and as of 2021-05-04, Metacran [17] lists 16 ...
Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping. [3] [4] [5] As of November 2018, the tidyverse package and some of its individual packages comprise 5 out of the top 10 most downloaded R packages. [6] The tidyverse is the subject of multiple books and papers.
These can be arranged into a 2×2 contingency table (confusion matrix), conventionally with the test result on the vertical axis and the actual condition on the horizontal axis. These numbers can then be totaled, yielding both a grand total and marginal totals. Totaling the entire table, the number of true positives, false negatives, true ...
Rmetrics is a free and open-source software project designed for teaching computational finance.Rmetrics is based primarily on the statistical R programming language, but does contain contributions in other programming languages, such as Fortran, C, and C++.
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A matrix showing the predicted and actual classifications. A confusion matrix is of size l × l, where l is the number of different label values. The following confusion matrix is for l = 2: followed by the matrix. It does not, however, state that that is the standard convention, the matrix could be merely an example.
An implementation of several whitening procedures in R, including ZCA-whitening and PCA whitening but also CCA whitening, is available in the "whitening" R package [7] published on CRAN. The R package "pfica" [8] allows the computation of high-dimensional whitening representations using basis function systems (B-splines, Fourier basis, etc.).