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Recall (information Retrieval)
In pattern recognition, information information retrieval, retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a data store, collection, Text_corpus, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. Written as a formula: \text = \frac Recall (also known as Sensitivity and specificity, sensitivity) is the fraction of relevant instances that were retrieved. Written as a formula: \text = \frac Both precision and recall are therefore based on Relevance (information retrieval), relevance. Consider a computer program for recognizing dogs (the relevant element) in a digital photograph. Upon processing a picture which contains ten cats and twelve dogs, the program identifies eight dogs. Of the eight elements identified as dogs, only five actually are dogs (True positive, true positives), while t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Receiver Operating Characteristic
A receiver operating characteristic curve, or ROC curve, is a graph of a function, graphical plot that illustrates the performance of a binary classifier model (can be used for multi class classification as well) at varying threshold values. ROC analysis is commonly applied in the assessment of diagnostic test performance in clinical epidemiology. The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting. The ROC can also be thought of as a plot of the statistical power as a function of the Type I Error of the decision rule (when the performance is calculated from just a sample of the population, it can be thought of as estimators of these quantities). The ROC curve is thus the sensitivity as a function of false positive rate. Given that the probability distributions for both true positive and false positive are known, the ROC curve is obtained as the cumulative distribution function (CDF, area under the probability ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
True Positive Rate
In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true positives and specificity is a measure of how well a test can identify true negatives: * Sensitivity (true positive rate) is the probability of a positive test result, conditioned on the individual truly being positive. * Specificity (true negative rate) is the probability of a negative test result, conditioned on the individual truly being negative. If the true status of the condition cannot be known, sensitivity and specificity can be defined relative to a " gold standard test" which is assumed correct. For all testing, both diagnoses and screening, there is usually a trade-off between sensitivity and specificity, such that higher sensiti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Accuracy (binary Classification)
Accuracy and precision are two measures of ''observational error''. ''Accuracy'' is how close a given set of measurements (observations or readings) are to their ''true value''. ''Precision'' is how close the measurements are to each other. The International Organization for Standardization (ISO) defines a related measure: ''trueness'', "the closeness of agreement between the arithmetic mean of a large number of test results and the true or accepted reference value." While ''precision'' is a description of '' random errors'' (a measure of statistical variability), ''accuracy'' has two different definitions: # More commonly, a description of ''systematic errors'' (a measure of statistical bias of a given measure of central tendency, such as the mean). In this definition of "accuracy", the concept is independent of "precision", so a particular set of data can be said to be accurate, precise, both, or neither. This concept corresponds to ISO's ''trueness''. # A combination of b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Markedness
In linguistics and social sciences, markedness is the state of standing out as nontypical or divergent as opposed to regular or common. In a marked–unmarked relation, one term of an opposition is the broader, dominant one. The dominant default or minimum-effort form is known as ''unmarked''; the other, secondary one is ''marked''. In other words, markedness involves the characterization of a "normal" linguistic unit against one or more of its possible "irregular" forms. In linguistics, markedness can apply to, among others, phonological, grammatical, and semantic oppositions, defining them in terms of marked and unmarked oppositions, such as ''honest'' (unmarked) vs. ''dishonest'' (marked). Marking may be purely semantic, or may be realized as extra morphology. The term derives from the marking of a grammatical role with a suffix or another element, and has been extended to situations where there is no morphological distinction. In social sciences more broadly, markedness ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Informedness
Youden's J statistic (also called Youden's index) is a single statistic that captures the performance of a dichotomous diagnostic test. In meteorology, this statistic is referred to as Peirce Skill Score (PSS), Hanssen–Kuipers Discriminant (HKD), or True Skill Statistic (TSS). (Bookmaker) Informedness is its generalization to the multiclass case and estimates the probability of an informed decision. Definition Youden's ''J'' statistic is : J = \text + \text -1=\text_1 + \text_0 -1 with the two right-hand quantities being sensitivity and specificity. Thus the expanded formula is: :J = \frac+\frac-1 = \frac In this equation, TP is the number of true positives, TN the number of true negatives, FP the number of false positives and FN the number of false negatives. The index was suggested by W. J. Youden in 1950 as a way of summarising the performance of a diagnostic test; however, the formula was earlier published in ''Science'' by C. S. Peirce in 1884. Its value range ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Regression Coefficient
In statistics, linear regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory variables (regressor or independent variable). A model with exactly one explanatory variable is a ''simple linear regression''; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression, which predicts multiple correlated dependent variables rather than a single dependent variable. In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probab ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Geometric Mean
In mathematics, the geometric mean is a mean or average which indicates a central tendency of a finite collection of positive real numbers by using the product of their values (as opposed to the arithmetic mean which uses their sum). The geometric mean of numbers is the Nth root, th root of their product (mathematics), product, i.e., for a collection of numbers , the geometric mean is defined as : \sqrt[n]. When the collection of numbers and their geometric mean are plotted in logarithmic scale, the geometric mean is transformed into an arithmetic mean, so the geometric mean can equivalently be calculated by taking the natural logarithm of each number, finding the arithmetic mean of the logarithms, and then returning the result to linear scale using the exponential function , :\sqrt[n] = \exp \left( \frac \right). The geometric mean of two numbers is the square root of their product, for example with numbers and the geometric mean is \textstyle \sqrt = The geometric mean o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Matthews Correlation Coefficient
In statistics, the phi coefficient, or mean square contingency coefficient, 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. Introduced by Karl Pearson,Cramer, H. (1946). ''Mathematical Methods of Statistics''. Princeton: Princeton University Press, p. 282 (second paragraph). https://archive.org/details/in.ernet.dli.2015.223699 and also known as the ''Yule phi coefficient'' from its introduction by Udny Yule in 1912 this measure is similar to the Pearson correlation coefficient in its interpretation. In meteorology, the phi coefficient, or its square (the latter aligning with M. H. Doolittle's original proposition from 1885), is referred to as the Doolittle Skill Score or the Doolittle Measure of Association. Definition A Pear ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Harmonic Mean
In mathematics, the harmonic mean is a kind of average, one of the Pythagorean means. It is the most appropriate average for ratios and rate (mathematics), rates such as speeds, and is normally only used for positive arguments. The harmonic mean is the multiplicative inverse, reciprocal of the arithmetic mean of the reciprocals of the numbers, that is, the generalized f-mean with f(x) = \frac. For example, the harmonic mean of 1, 4, and 4 is :\left(\frac\right)^ = \frac = \frac = 2\,. Definition The harmonic mean ''H'' of the positive real numbers x_1, x_2, \ldots, x_n is :H(x_1, x_2, \ldots, x_n) = \frac = \frac. It is the reciprocal of the arithmetic mean of the reciprocals, and vice versa: :\begin H(x_1, x_2, \ldots, x_n) &= \frac, \\ A(x_1, x_2, \ldots, x_n) &= \frac, \end where the arithmetic mean is A(x_1, x_2, \ldots, x_n) = \tfrac1n \sum_^n x_i. The harmonic mean is a Schur-concave function, and is greater than or equal to the minimum of its arguments: for positive a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
