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  2. Phi coefficient - Wikipedia

    en.wikipedia.org/wiki/Phi_coefficient

    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.

  3. Golden ratio - Wikipedia

    en.wikipedia.org/wiki/Golden_ratio

    where the Greek letter phi (⁠ ⁠ or ⁠ ⁠) denotes the golden ratio. [ a ] The constant ⁠ φ {\displaystyle \varphi } ⁠ satisfies the quadratic equation ⁠ φ 2 = φ + 1 {\displaystyle \textstyle \varphi ^{2}=\varphi +1} ⁠ and is an irrational number with a value of [ 1 ]

  4. Euler's totient function - Wikipedia

    en.wikipedia.org/wiki/Euler's_totient_function

    Thus, it is often called Euler's phi function or simply the phi function. In 1879, J. J. Sylvester coined the term totient for this function, [14] [15] so it is also referred to as Euler's totient function, the Euler totient, or Euler's totient. [16] Jordan's totient is a generalization of Euler's. The cototient of n is defined as n − φ(n).

  5. Evaluation of binary classifiers - Wikipedia

    en.wikipedia.org/wiki/Evaluation_of_binary...

    The relationship between sensitivity and specificity, as well as the performance of the classifier, can be visualized and studied using the Receiver Operating Characteristic (ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both (such as in the red/blue ball example given above).

  6. Illusory truth effect - Wikipedia

    en.wikipedia.org/wiki/Illusory_truth_effect

    The conclusion made by the researchers was that repeating a statement makes it more likely to appear factual. [1] [2] In 1989, Hal R. Arkes, Catherine Hackett, and Larry Boehm replicated the original study, with similar results showing that exposure to false information changes the perceived truthfulness and plausibility of that information. [6]

  7. Effect size - Wikipedia

    en.wikipedia.org/wiki/Effect_size

    In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of one parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size ...

  8. Faraday's law of induction - Wikipedia

    en.wikipedia.org/wiki/Faraday's_law_of_induction

    This statement, however, is not always true and the reason is not just from the obvious reason that emf is undefined in empty space when no conductor is present. As noted in the previous section, Faraday's law is not guaranteed to work unless the velocity of the abstract curve ∂Σ matches the actual velocity of the material conducting the ...

  9. Correlation coefficient - Wikipedia

    en.wikipedia.org/wiki/Correlation_coefficient

    A correlation coefficient is a numerical measure of some type of linear correlation, meaning a statistical relationship between two variables. [a] The variables may be two columns of a given data set of observations, often called a sample, or two components of a multivariate random variable with a known distribution. [citation needed]