Error De Precision Wikipedia
systematic errors, a measure of statistical bias; alternatively, ISO defines accuracy as describing both types of observational error above (preferring the term trueness for the common definition of accuracy). Contents 1 Common definition 1.1 Quantification 2 ISO definition (ISO 5725) 3 In binary classification 4 In psychometrics and psychophysics 5 In logic simulation 6 In information systems 7 See also 8 References 9 External links Common definition[edit] Accuracy is the proximity of measurement results to the true value; precision, the repeatability, or reproducibility of the measurement In the fields of science, engineering and statistics, the accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity's true value.[1] The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.[1][2] Although the two words precision and accuracy can be synonymous in colloquial use, they are deliberately contrasted in the context of the scientific method. A measurement system can be accurate but not precise, precise but not accurate, neither, or both. For example, if an experiment contains a systematic error, then increasing the sample size generally increases precision but does not improve accuracy. The result would be a consistent yet inaccurate string of results from the flawed experiment. Eliminating the systematic error improves accuracy but does not change precision. A measurement system is considered valid if it is both accurate and precise. Related terms include bias (non-random or directed effects caused by a factor or factors unrelated to the independent variable) and error (random variability). The terminology is also applied to indirect measurements—that is, values obtained by a computational procedure from observed data. In addition to accuracy and precision, measurements may also have a measurement resolution, which is the smallest change in the underlying physical quantity that produces a response in the measurement. In numerical analysis, accuracy is also the nearness of a calculation to the true value; while precision is the resolution of the representation, typically defined by the number of decimal or binary digits. Statistical literature prefers to use the terms bias and variability instead of accuracy and precision: bias is the amount of inaccuracy and variability
Sensitivity (also called the true positive rate, the recall, or probability of detection[1] in some fields) measures the proportion of positives that are correctly identified as such (e.g., the percentage of sick people who are correctly identified as having the condition). Specificity (also called the true negative rate) measures the proportion of negatives that are correctly identified as such (e.g., the percentage of healthy people who are correctly identified as not having the condition). Thus sensitivity quantifies the avoiding of false negatives, as specificity does for false positives. For any test, there is usually a trade-off between the measures. For instance, in an airport security setting in https://en.wikipedia.org/wiki/Accuracy_and_precision which one is testing for potential threats to safety, scanners may be set to trigger on low-risk items like belt buckles and keys (low specificity), in order to reduce the risk of missing objects that do pose a threat to the aircraft and those aboard (high sensitivity). This trade-off can be represented graphically as a receiver operating characteristic curve. A perfect predictor would be described as 100% sensitive (e.g., all https://en.wikipedia.org/wiki/Sensitivity_and_specificity sick are identified as sick) and 100% specific (e.g., no healthy are identified as sick); however, theoretically any predictor will possess a minimum error bound known as the Bayes error rate. Contents 1 Definitions 1.1 Sensitivity 1.2 Specificity 1.3 Graphical illustration 2 Medical examples 2.1 Misconceptions 2.2 Sensitivity index 3 Worked example 4 Estimation of errors in quoted sensitivity or specificity 5 Terminology in information retrieval 6 See also 7 References 8 Further reading 9 External links Definitions[edit] Terminology and derivations from a confusion matrix true positive (TP) eqv. with hit true negative (TN) eqv. with correct rejection false positive (FP) eqv. with false alarm, Type I error false negative (FN) eqv. with miss, Type II error sensitivity or true positive rate (TPR) eqv. with hit rate, recall T P R = T P / P = T P / ( T P + F N ) {\displaystyle {\mathit {TPR}}={\mathit {TP}}/P={\mathit {TP}}/({\mathit {TP}}+{\mathit {FN}})} specificity (SPC) or true negative rate S P C = T N / N = T N / ( T N + F P ) {\displaystyle {\mathit {SPC}}={\mathit {TN}}/N={\mathit {TN}}/({\mathit {TN}}+{\mathit {FP}})} precision or positive predictive value (PPV) P P V = T P / ( T P + F P ) {\
the fraction of retrieved instances that are relevant, while recall (also known as sensitivity) is the fraction of relevant instances that are retrieved. Both precision and recall are therefore based on an understanding https://en.wikipedia.org/wiki/Precision_and_recall and measure of relevance. Suppose a computer program for recognizing dogs in https://en.wikipedia.org/wiki/False_precision scenes from a video identifies 7 dogs in a scene containing 9 dogs and some cats. If 4 of the identifications are correct, but 3 are actually cats, the program's precision is 4/7 while its recall is 4/9. When a search engine returns 30 pages only 20 of which were error de relevant while failing to return 40 additional relevant pages, its precision is 20/30 = 2/3 while its recall is 20/60 = 1/3. So, in this case, precision is "how useful the search results are", and recall is "how complete the results are". In statistics, if the null hypothesis is that all and only the relevant items are retrieved, absence of type I and error de precision type II errors corresponds respectively to maximum precision (no false positive) and maximum recall (no false negative). The above pattern recognition example contained 7 − 4 = 3 type I errors and 9 − 4 = 5 type II errors. Precision can be seen as a measure of exactness or quality, whereas recall is a measure of completeness or quantity. In simple terms, high precision means that an algorithm returned substantially more relevant results than irrelevant, while high recall means that an algorithm returned most of the relevant results. Contents 1 Introduction 2 Definition (information retrieval context) 2.1 Precision 2.2 Recall 3 Definition (classification context) 4 Probabilistic interpretation 5 F-measure 6 Limitations as goals 7 See also 8 References 9 External links Introduction[edit] In an information retrieval scenario, the instances are documents and the task is to return a set of relevant documents given a search term; or equivalently, to assign each document to one of two categories, "relevant" and "not relevant". In this case, the "relevant" documents are simply those that belong to the "relevant" category. Recall is defined as the number of relevant doc
Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (October 2015) (Learn how and when to remove this template message) Fit approximation Concepts Orders of approximation Scale analysis· Big O notation Curve fitting· False precision Significant figures Other fundamentals Approximation· Generalization error Taylor polynomial Scientific modelling v t e False precision (also called overprecision, fake precision, misplaced precision and spurious accuracy) occurs when numerical data are presented in a manner that implies better precision than is actually the case; since precision is a limit to accuracy, this often leads to overconfidence in the accuracy as well.[1] Contents 1 Overview 2 Examples 3 See also 4 References 5 External links Overview[edit] Madsen Pirie defines the term "false precision" in a more general way: when exact numbers are used for notions that cannot be expressed in exact terms. For example, "I am 90% sure he is wrong". Often false precision is abused to produce an unwarranted confidence in the claim: "our mouthwash is twice as good as our competitor's". [2] In science and engineering, convention dictates that unless a margin of error is explicitly stated, the number of significant figures used in the presentation of data should be limited to what is warranted by the precision of those data. For example, if an instrument can be read to tenths of a unit of measurement, results of calculations using data obtained from that instrument can only be confidently stated to the tenths place, regardless of what the raw calculation returns or whether other data used in the calculation are more accurate. Even outside these disciplines, there is a tendency to assume that all the non-zero digits of a number are meaningful; thus, providing excessive figures may lead the viewer to expect better precision than actually exists. However, in contrast, it is good practice to retain more significant figures than this in the intermediate stages of a calculation, in order to avoid accumulated rounding errors. False precision commonly arises when high-precision and low-precision data are combined, and in conversi