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Confusion matrix is not limited to binary classification and can be used in multi-class classifiers as well. The confusion matrices discussed above have only two conditions: positive and negative. For example, the table below summarizes communication of a whistled language between two speakers, with zero values omitted for clarity. [24]
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 ...
Pascal uses the caret for declaring and dereferencing pointers. In Smalltalk, the caret is the method return statement. In C++/CLI, .NET reference types are accessed through a handle using the ClassName^ syntax. In Apple's C extensions for Mac OS X and iOS, carets are used to create blocks and to denote block types. Go uses it as a bitwise NOT ...
Overhead Imagery Research Data Set: Annotated overhead imagery. Images with multiple objects. Over 30 annotations and over 60 statistics that describe the target within the context of the image. 1000 Images, text Classification 2009 [166] [167] F. Tanner et al. SpaceNet SpaceNet is a corpus of commercial satellite imagery and labeled training data.
5 data sets that center around robotic failure to execute common tasks. Integer valued features such as torque and other sensor measurements. 463 Text Classification 1999 [206] L. Seabra et al. Pittsburgh Bridges Dataset Design description is given in terms of several properties of various bridges. Various bridge features are given. 108 Text
CARET (Computerized Anatomical Reconstruction Toolkit) is a software application for the structural and functional analysis of the cerebral and cerebellar cortex. CARET is developed in the Van Essen Laboratory in the Department of Anatomy and Neurobiology at the Washington University School of Medicine in St. Louis, Missouri .
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
The C language provides the four basic arithmetic type specifiers char, int, float and double (as well as the boolean type bool), and the modifiers signed, unsigned, short, and long. The following table lists the permissible combinations in specifying a large set of storage size-specific declarations.