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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. [20]
In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or r φ) is a measure of association for two binary variables.. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.
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 ...
The resulting number gives an estimate on how many positive examples the feature could correctly identify within the data, with higher numbers meaning that the feature could correctly classify more positive samples. Below is an example of how to use the metric when the full confusion matrix of a certain feature is given: Feature A Confusion Matrix
Confusion matrix; Pivot table, in spreadsheet software, cross-tabulates sampling data with counts (contingency table) and/or sums. TPL Tables is a tool for generating and printing crosstabs. The iterative proportional fitting procedure essentially manipulates contingency tables to match altered joint distributions or marginal sums.
A chord diagram is a graphical method of displaying the inter-relationships between data in a matrix. The data are arranged radially around a circle with the relationships between the data points typically drawn as arcs connecting the data. The format can be aesthetically pleasing, making it a popular choice in the world of data visualization.
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
Data and information visualization (data viz/vis or info viz/vis) [2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount [3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items.