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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.
Phi is related to the point-biserial correlation coefficient and Cohen's d and estimates the extent of the relationship between two variables (2 × 2). [32] Cramér's V may be used with variables having more than two levels. Phi can be computed by finding the square root of the chi-squared statistic divided by the sample size.
Phi coefficient. A simple measure, applicable only to the case of 2 × 2 contingency tables, is the phi coefficient (φ) defined by =, where χ 2 is computed as in ...
the population mean or expected value in probability and statistics; a measure in measure theory; micro-, an SI prefix denoting 10 −6 (one millionth) Micrometre or micron (retired in 1967 as a standalone symbol, replaced by "μm" using the standard SI meaning) the coefficient of friction in physics; the service rate in queueing theory
In statistics, Cramér's V (sometimes referred to as Cramér's phi and denoted as φ c) is a measure of association between two nominal variables, giving a value between 0 and +1 (inclusive). It is based on Pearson's chi-squared statistic and was published by Harald Cramér in 1946.
Archaic form of Phi. Phi (/ f aɪ /; [1] uppercase Φ, lowercase φ or ϕ; Ancient Greek: ϕεῖ pheî; Modern Greek: φι fi) is the twenty-first letter of the Greek alphabet.. In Archaic and Classical Greek (c. 9th to 4th century BC), it represented an aspirated voiceless bilabial plosive ([pʰ]), which was the origin of its usual romanization as ph .
pAUC computed in the region where Phi>0.35. Several performance metrics are available for binary classifiers. One of the most popular is the Phi coefficient [8] (also known as the Matthews Correlation Coefficient [9]). Phi measures how better (or worse) is a classification, with respect to the random classification, which is characterized by ...
Euclid's Elements (c. 300 BC) provides several propositions and their proofs employing the golden ratio, [15] [c] and contains its first known definition which proceeds as follows: [16] A straight line is said to have been cut in extreme and mean ratio when, as the whole line is to the greater segment, so is the greater to the lesser. [17] [d]