109:
387:
375:
31:
4583:
2247:
779:
2254:
485:. Where not explicitly stated, the margin of error is understood to be one-half the value of the last significant place. For instance, a recording of 843.6 m, or 843.0 m, or 800.0 m would imply a margin of 0.05 m (the last significant place is the tenths place), while a recording of 843 m would imply a margin of error of 0.5 m (the last significant digits are the units).
489:
significant (hence a margin of 50 m) while 8.000 Ă— 10 m indicates that all three zeros are significant, giving a margin of 0.5 m. Similarly, one can use a multiple of the basic measurement unit: 8.0 km is equivalent to 8.0 Ă— 10 m. It indicates a margin of 0.05 km (50 m). However, reliance on this convention can lead to
349:, it involves a component of random error and a component of systematic error. In this case trueness is the closeness of the mean of a set of measurement results to the actual (true) value, that is the systematic error, and precision is the closeness of agreement among a set of results, that is the random error.
836:
relevant results selected by humans. Recall is defined as the fraction of documents correctly retrieved compared to the relevant documents (true positives divided by true positives plus false negatives). Less commonly, the metric of accuracy is used, is defined as the fraction of documents correctly
660:
In this context, the concepts of trueness and precision as defined by ISO 5725-1 are not applicable. One reason is that there is not a single “true value” of a quantity, but rather two possible true values for every case, while accuracy is an average across all cases and therefore takes into account
721:
evaluation. To evaluate top-5 accuracy, the classifier must provide relative likelihoods for each class. When these are sorted, a classification is considered correct if the correct classification falls anywhere within the top 5 predictions made by the network. Top-5 accuracy was popularized by
840:
None of these metrics take into account the ranking of results. Ranking is very important for web search engines because readers seldom go past the first page of results, and there are too many documents on the web to manually classify all of them as to whether they should be included or excluded
518:
In engineering, precision is often taken as three times
Standard Deviation of measurements taken, representing the range that 99.73% of measurements can occur within. For example, an ergonomist measuring the human body can be confident that 99.73% of their extracted measurements fall within ±
488:
A reading of 8,000 m, with trailing zeros and no decimal point, is ambiguous; the trailing zeros may or may not be intended as significant figures. To avoid this ambiguity, the number could be represented in scientific notation: 8.0 Ă— 10 m indicates that the first zero is
828:, which divides results into true positives (documents correctly retrieved), true negatives (documents correctly not retrieved), false positives (documents incorrectly retrieved), and false negatives (documents incorrectly not retrieved). Commonly used metrics include the notions of
857:
In cognitive systems, accuracy and precision is used to characterize and measure results of a cognitive process performed by biological or artificial entities where a cognitive process is a transformation of data, information, knowledge, or wisdom to a higher-valued form.
766:. The validity of a measurement instrument or psychological test is established through experiment or correlation with behavior. Reliability is established with a variety of statistical techniques, classically through an internal consistency test like
712:
415:
Ideally a measurement device is both accurate and precise, with measurements all close to and tightly clustered around the true value. The accuracy and precision of a measurement process is usually established by repeatedly measuring some
159:
A combination of both precision and trueness, accounting for the two types of observational error (random and systematic), so that high accuracy requires both high precision and high trueness. This usage corresponds to ISO's definition of
270:
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.
493:
errors when accepting data from sources that do not obey it. For example, a source reporting a number like 153,753 with precision +/- 5,000 looks like it has precision +/- 0.5. Under the convention it would have been rounded to 150,000.
862:) Sometimes, a cognitive process produces exactly the intended or desired output but sometimes produces output far from the intended or desired. Furthermore, repetitions of a cognitive process do not always produce the same output.
497:
Alternatively, in a scientific context, if it is desired to indicate the margin of error with more precision, one can use a notation such as 7.54398(23) Ă— 10 m, meaning a range of between 7.54375 and 7.54421 Ă— 10 m.
152:, 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
411:
In industrial instrumentation, accuracy is the measurement tolerance, or transmission of the instrument and defines the limits of the errors made when the instrument is used in normal operating conditions.
832:. In this context, precision is defined as the fraction of documents correctly retrieved compared to the documents retrieved (true positives divided by true positives plus false positives), using a set of
640:
882:
in human/cog ensembles, where one or more humans work collaboratively with one or more cognitive systems (cogs), increases in cognitive accuracy and cognitive precision assist in measuring the degree of
3909:
682:
112:
According to ISO 5725-1, accuracy consists of trueness (proximity of the mean of measurement results to the true value) and precision (repeatability or reproducibility of the measurement).
447:
is properly applied: the precision of the average is equal to the known standard deviation of the process divided by the square root of the number of measurements averaged. Further, the
319:, 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.
345:
According to ISO 5725-1, the general term "accuracy" is used to describe the closeness of a measurement to the true value. When the term is applied to sets of measurements of the same
726:
challenge. It is usually higher than top-1 accuracy, as any correct predictions in the 2nd through 5th positions will not improve the top-1 score, but do improve the top-5 score.
1638:
1241:
1258:
338:
A shift in the meaning of these terms appeared with the publication of the ISO 5725 series of standards in 1994, which is also reflected in the 2008 issue of the BIPM
508:— the variation arising when all efforts are made to keep conditions constant by using the same instrument and operator, and repeating during a short time period; and
837:
classified compared to the documents (true positives plus true negatives divided by true positives plus true negatives plus false positives plus false negatives).
1968:
801:. This is a comparison of differences in precision, not accuracy. Precision is measured with respect to detail and accuracy is measured with respect to reality.
3421:
1631:
564:
3416:
770:
to ensure sets of related questions have related responses, and then comparison of those related question between reference and target population.
356:", previously specified in BS 5497-1, because it has different connotations outside the fields of science and engineering, as in medicine and law.
714:
This is usually expressed as a percentage. For example, if a classifier makes ten predictions and nine of them are correct, the accuracy is 90%.
3638:
3426:
2921:
2299:
1006:
BS ISO 5725-1: "Accuracy (trueness and precision) of measurement methods and results - Part 1: General principles and definitions.", p.1 (1994)
437:
92:
1624:
1103:
BS 5497-1: "Precision of test methods. Guide for the determination of repeatability and reproducibility for a standard test method." (1979)
262:
A measurement system can be accurate but not precise, precise but not accurate, neither, or both. For example, if an experiment contains a
1767:
3143:
821:
2040:
1289:
1016:
Menditto, Antonio; Patriarca, Marina; Magnusson, Bertil (2007-01-09). "Understanding the meaning of accuracy, trueness and precision".
4113:
4218:
4138:
3929:
3924:
3770:
3936:
1674:
514:— the variation arising using the same measurement process among different instruments and operators, and over longer time periods.
305:
The terminology is also applied to indirect measurements—that is, values obtained by a computational procedure from observed data.
943:
533:
2290:
1782:
1265:
1210:
845:, for example, is a measure of precision looking only at the top ten (k=10) search results. More sophisticated metrics, such as
4469:
2659:
1336:
841:
from a given search. Adding a cutoff at a particular number of results takes ranking into account to some degree. The measure
2831:
1535:
1480:
1136:
339:
386:
4538:
4339:
3862:
3245:
2836:
2806:
4183:
3513:
3310:
545:
test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both
374:
2634:
1433:
937:
2095:
3710:
2734:
2257:
2206:
1772:
1079:
923:
4274:
3446:
3290:
326:), the precision of fire expressed by the closeness of a grouping of shots at and around the centre of the target.
679:
When computing accuracy in multiclass classification, accuracy is simply the fraction of correct classifications:
259:
instead of accuracy and precision: bias is the amount of inaccuracy and variability is the amount of imprecision.
4607:
1994:
1855:
903:
2201:
1094:
North
Atlantic Treaty Organization, NATO Standardization Agency AAP-6 – Glossary of terms and definitions, p 43.
312:, which is the smallest change in the underlying physical quantity that produces a response in the measurement.
4369:
4364:
4123:
4071:
2151:
2033:
1679:
1361:
554:
481:
A common convention in science and engineering is to express accuracy and/or precision implicitly by means of
4163:
3493:
3006:
2654:
2171:
2166:
1689:
1405:
Glasser, Mark; Mathews, Rob; Acken, John M. (June 1990). "1990 Workshop on Logic-Level
Modelling for ASICS".
791:
1945:
1704:
1567:
Guide to the
Expression of Uncertainty in Measurement (GUM) and International Vocabulary of Metrology (VIM)
968:
718:
455:
of the averaged measurements will be closer to a normal distribution than that of individual measurements.
177:
or set of data points from repeated measurements of the same quantity, the sample or set can be said to be
4508:
3986:
2283:
2222:
2120:
1911:
1840:
1777:
973:
444:
667:
is used in this context to mean a different metric originating from the field of information retrieval (
4168:
4076:
4026:
3785:
3118:
3113:
2512:
2232:
1724:
1699:
1684:
918:
846:
2075:
707:{\displaystyle {\text{Accuracy}}={\frac {\text{correct classifications}}{\text{all classifications}}}}
4632:
4434:
4379:
4158:
4143:
3401:
2821:
2317:
2080:
2026:
1714:
1709:
1160:
Parker, Christopher J.; Gill, Simeon; Harwood, Adrian; Hayes, Steven G.; Ahmed, Maryam (2021-05-19).
933:
1055:
519:
0.7 cm - if using the GRYPHON processing system - or ± 13 cm - if using unprocessed data.
4482:
4389:
4178:
4153:
3365:
3360:
2227:
1810:
983:
759:
452:
255:
17:
4036:
3578:
3568:
3563:
3138:
3123:
3063:
2497:
2492:
2452:
2141:
1901:
1805:
1800:
953:
849:, take into account each individual ranking, and are more commonly used where this is important.
557:. To make the context clear by the semantics, it is often referred to as the "Rand accuracy" or "
127:
4627:
4622:
4612:
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4354:
4344:
3473:
2472:
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2176:
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958:
897:
884:
433:
309:
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3285:
2507:
2477:
2370:
2360:
2321:
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747:
542:
448:
4533:
4439:
4173:
4148:
3991:
3832:
3583:
3270:
3073:
2946:
2729:
2105:
2085:
1963:
1863:
1752:
1747:
1510:
Human
Interface and the Management of Information. Information in Applications and Services
829:
663:
467:
295:
185:
is close to the true value of the quantity being measured, while the set can be said to be
247:, where the interpretation of measurements plays a central role, prefers to use the terms
8:
4374:
1669:
1656:
978:
948:
879:
767:
482:
362:
213:
58:
1162:"A Method for Increasing 3D Body Scanning's Precision: Gryphon and Consecutive Scanning"
717:
Accuracy is also called top-1 accuracy to distinguish it from top-5 accuracy, common in
224:, is the degree to which repeated measurements under unchanged conditions show the same
4617:
4319:
4254:
2594:
1989:
1955:
1835:
1664:
1541:
1513:
1512:. Lecture Notes in Computer Science. Vol. 10905. Springer Cham. pp. 494–507.
1486:
1458:
1457:. Lecture Notes in Computer Science. Vol. 11580. Springer Cham. pp. 533–545.
1304:
421:
316:
190:
174:
1616:
1586:
1229:
1071:
An
Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements
870:) is the propensity of a cognitive process to produce the intended or desired output.
443:
This also applies when measurements are repeated and averaged. In that case, the term
3148:
2110:
1984:
1873:
1820:
1531:
1490:
1476:
1429:
1325:
1283:
1233:
1191:
1183:
1142:
1132:
1075:
1033:
817:
798:
353:
249:
237:
140:
1591:
Guidelines for
Evaluating and Expressing the Uncertainty of NIST Measurement Results
1545:
108:
3882:
3623:
2951:
2268:
2049:
2007:
1792:
1757:
1694:
1647:
1610:
1571:
1523:
1468:
1225:
1173:
1025:
825:
787:
263:
149:
145:
1595:
1178:
1161:
878:) is the propensity of a cognitive process to produce the same output. To measure
561:". It is a parameter of the test. The formula for quantifying binary accuracy is:
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4213:
4118:
3872:
3800:
3795:
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3275:
3238:
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3223:
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2070:
1940:
1883:
1762:
1527:
1472:
490:
406:
217:
102:
46:
1426:
Democratization of
Expertise: How Cognitive Systems Will Revolutionize Your Life
3780:
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2115:
1505:
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842:
1606:
1600:
1562:
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Comparative
Waveforms for Logic values, circuit voltages, and measure voltages
553:) among the total number of cases examined. As such, it compares estimates of
4601:
4568:
4543:
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4459:
4454:
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4444:
4289:
4234:
4203:
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4016:
4011:
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3904:
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2375:
2345:
1825:
1187:
1037:
739:
735:
550:
546:
221:
122:
105:
of a large number of test results and the true or accepted reference value."
42:
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4399:
4259:
3603:
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3315:
3265:
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3153:
3128:
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2996:
2961:
2871:
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2502:
2196:
2146:
1195:
908:
859:
833:
417:
69:
4404:
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3675:
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2100:
2090:
1237:
963:
471:
267:
205:
201:
73:
30:
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1919:
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558:
244:
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78:
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322:
In military terms, accuracy refers primarily to the accuracy of fire (
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3613:
3608:
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3021:
3016:
2911:
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2552:
2547:
2542:
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2445:
2440:
2435:
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2136:
1737:
346:
790:, a common mistake in evaluation of accurate models is to compare a
308:
In addition to accuracy and precision, measurements may also have a
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3508:
3345:
3335:
3320:
3295:
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3133:
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3083:
3078:
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3033:
3028:
3011:
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2986:
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2971:
2966:
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2916:
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2886:
2881:
2876:
2861:
2856:
2851:
2846:
2841:
2826:
2811:
2784:
2779:
2774:
2769:
2764:
2759:
2754:
2744:
2739:
2719:
2714:
2704:
2699:
2689:
2684:
2674:
2669:
2664:
2639:
2629:
2522:
2425:
2420:
2415:
2410:
2405:
2400:
2395:
2390:
2385:
2018:
1518:
1463:
1309:
813:
723:
294:
or directed effects caused by a factor or factors unrelated to the
209:
1362:"3.3. Metrics and scoring: quantifying the quality of predictions"
1303:
Powers, David M. W. (2015). "What the F-measure doesn't measure".
4487:
4279:
4264:
4061:
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4041:
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2624:
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2609:
2604:
2589:
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2574:
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2532:
2487:
2482:
2467:
2462:
2457:
1845:
1732:
928:
778:
197:
182:
49:) measurements under unchanged conditions show the same results.
4349:
4334:
4329:
4324:
3996:
3971:
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3755:
3740:
3720:
3715:
3660:
3653:
3648:
3643:
3538:
3503:
3350:
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2816:
2380:
291:
225:
37:
is the proximity of measurement results to the accepted value;
635:{\displaystyle {\text{Accuracy}}={\frac {TP+TN}{TP+TN+FP+FN}}}
4384:
4359:
4081:
3966:
3837:
3618:
3483:
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3330:
3325:
3305:
2679:
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236:
use, they are deliberately contrasted in the context of the
4031:
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1924:
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1868:
463:
1015:
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1582:
Precision and
Accuracy with Three Psychophysical Methods
1572:"Beyond NIST Traceability: What really creates accuracy"
470:. Establishing and correcting for bias is necessary for
228:. Although the two words precision and accuracy can be
1646:
1058:— Basic and general concepts and associated terms (VIM)
425:
208:
system is the degree of closeness of measurements of a
1159:
534:
Evaluation of binary classifiers § Single metrics
1607:
What's the difference between accuracy and precision?
729:
685:
567:
2298:
541:
is also used as a statistical measure of how well a
216:. The precision of a measurement system, related to
1506:"On Measuring Cognition and Cognitive Augmentation"
1451:"Calculating Cognitive Augmentation – A Case Study"
1404:
1056:
JCGM 200:2008 International vocabulary of metrology
352:
ISO 5725-1 and VIM also avoid the use of the term "
1128:DeGarmo's materials and processes in manufacturing
706:
634:
400:
1051:
1049:
1047:
466:of the measurements and the reference value, the
88:is how close the measurements are to each other.
4599:
1388:Encyclopedia of Computer Science and Technology
674:
1074:. University Science Books. pp. 128–129.
1044:
438:National Institute of Standards and Technology
168:
93:International Organization for Standardization
2284:
2034:
1632:
1323:
458:With regard to accuracy we can distinguish:
329:
1428:(1st ed.). Boca Raton, FL: CRC Press.
668:
527:
2291:
2277:
2041:
2027:
1639:
1625:
477:the combined effect of that and precision.
101:, "the closeness of agreement between the
1517:
1503:
1462:
1448:
1423:
1308:
1177:
1002:
1000:
807:
777:
107:
29:
852:
812:Information retrieval systems, such as
14:
4600:
1302:
1288:: CS1 maint: archived copy as title (
1067:
997:
773:
2272:
2022:
1620:
1385:
1124:
1061:
824:, some of which are derived from the
392:Low accuracy even with high precision
340:International Vocabulary of Metrology
27:Characterization of measurement error
2253:
2048:
1208:
522:
424:. Such standards are defined in the
1018:Accreditation and Quality Assurance
1009:
274:A measurement system is considered
24:
1211:"Basic principles of ROC analysis"
900:in statistics and machine learning
730:In psychometrics and psychophysics
25:
4644:
2207:List of system quality attributes
1556:
1125:Black, J. Temple (21 July 2020).
924:Experimental uncertainty analysis
380:Low accuracy due to low precision
95:(ISO) defines a related measure:
4582:
4581:
2252:
2246:
2245:
1247:from the original on 2022-10-09.
385:
373:
138:More commonly, a description of
1995:Pearson correlation coefficient
1497:
1442:
1417:
1398:
1386:Acken, John M. (1997). "none".
1379:
1354:
1342:from the original on 2022-10-09
1317:
1296:
1251:
1202:
904:Accepted and experimental value
661:both values. However, the term
401:Quantification and applications
134:has two different definitions:
1603:— a brief video by Matt Parker
1153:
1118:
1106:
1097:
1088:
555:pre- and post-test probability
430:Système international d'unités
365:according to BIPM and ISO 5725
13:
1:
1934:Deep Learning Related Metrics
1230:10.1016/s0001-2998(78)80014-2
1179:10.1080/00140139.2021.1931473
990:
944:Hypothesis tests for accuracy
746:is interchangeably used with
432:) and maintained by national
428:(abbreviated SI from French:
426:International System of Units
1528:10.1007/978-3-319-92046-7_41
1473:10.1007/978-3-030-22419-6_38
1068:Taylor, John Robert (1999).
969:Random and systematic errors
719:convolutional neural network
675:In multiclass classification
342:(VIM), items 2.13 and 2.14.
68:is how close a given set of
7:
2223:Software quality management
2202:Non-functional requirements
1778:Sensitivity and specificity
974:Sensitivity and specificity
890:
462:the difference between the
169:Common technical definition
10:
4649:
2233:Software quality assurance
1563:BIPM - Guides in metrology
1114:InstrumentaciĂłn Industrial
919:Exactness (disambiguation)
847:discounted cumulative gain
531:
404:
173:In simpler terms, given a
76:or readings) are to their
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4501:
4227:
3379:
2328:
2310:
2241:
2215:
2189:
2129:
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2003:
1977:
1954:
1933:
1910:
1882:
1854:
1791:
1723:
1655:
1587:Appendix D.1: Terminology
1209:Metz, CE (October 1978).
1131:. John Wiley & Sons.
1030:10.1007/s00769-006-0191-z
934:Floating point arithmetic
332:ISO definition (ISO 5725)
164:(trueness and precision).
2228:Software quality control
1326:"The Problem with Kappa"
984:Statistical significance
799:circuit simulation model
528:In binary classification
453:probability distribution
286:. Related terms include
212:to that quantity's true
1806:Calinski-Harabasz index
1576:Controlled Environments
1504:Fulbright, Ron (2018).
1449:Fulbright, Ron (2019).
1424:Fulbright, Ron (2020).
954:Measurement uncertainty
697:correct classifications
434:standards organizations
128:statistical variability
41:is the degree to which
4608:Accuracy and precision
1596:Accuracy and Precision
959:Precision (statistics)
898:Bias-variance tradeoff
885:cognitive augmentation
822:many different metrics
808:In information systems
792:logic simulation model
783:
708:
636:
440:in the United States.
310:measurement resolution
302:(random variability).
266:, then increasing the
148:of a given measure of
113:
54:Accuracy and precision
50:
1969:Intra-list Similarity
1601:Accuracy vs Precision
914:Engineering tolerance
781:
709:
637:
543:binary classification
449:central limit theorem
193:is relatively small.
111:
33:
1609:by Matt Anticole at
1333:Anthology.aclweb.org
853:In cognitive systems
830:precision and recall
683:
565:
501:Precision includes:
296:independent variable
204:, the accuracy of a
120:is a description of
56:are two measures of
2190:Standards and lists
1455:Augmented Cognition
979:Significant figures
949:Information quality
880:augmented cognition
872:Cognitive precision
820:, are evaluated by
774:In logic simulation
700:all classifications
656:FN = False negative
648:FP = False positive
483:significant figures
59:observational error
2304:by standard number
1990:Euclidean distance
1956:Recommender system
1836:Similarity measure
1650:evaluation metrics
1324:David M W Powers.
864:Cognitive accuracy
818:web search engines
784:
704:
652:TN = True negative
644:TP = True positive
632:
317:numerical analysis
191:standard deviation
175:statistical sample
114:
51:
4595:
4594:
2318:ISO romanizations
2266:
2265:
2185:
2184:
2111:Understandability
2016:
2015:
1985:Cosine similarity
1821:Hopkins statistic
1537:978-3-319-92045-0
1482:978-3-030-22418-9
1138:978-1-119-72329-5
938:Accuracy problems
758:is a synonym for
702:
701:
698:
689:
630:
571:
523:In classification
238:scientific method
196:In the fields of
141:systematic errors
16:(Redirected from
4640:
4633:Software quality
4585:
4584:
2305:
2293:
2286:
2279:
2270:
2269:
2256:
2255:
2249:
2248:
2061:
2060:
2050:Software quality
2043:
2036:
2029:
2020:
2019:
2008:Confusion matrix
1783:Logarithmic Loss
1648:Machine learning
1641:
1634:
1627:
1618:
1617:
1550:
1549:
1521:
1501:
1495:
1494:
1466:
1446:
1440:
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1421:
1415:
1414:
1407:SIGDA Newsletter
1402:
1396:
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1383:
1377:
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1315:
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1312:
1300:
1294:
1293:
1287:
1279:
1277:
1276:
1270:
1264:. Archived from
1263:
1255:
1249:
1248:
1246:
1215:
1206:
1200:
1199:
1181:
1157:
1151:
1150:
1122:
1116:
1112:Creus, Antonio.
1110:
1104:
1101:
1095:
1092:
1086:
1085:
1065:
1059:
1053:
1042:
1041:
1013:
1007:
1004:
826:confusion matrix
788:logic simulation
768:Cronbach's alpha
713:
711:
710:
705:
703:
699:
696:
695:
690:
687:
657:
653:
649:
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633:
631:
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577:
572:
569:
389:
377:
334:
333:
264:systematic error
150:central tendency
146:statistical bias
21:
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3375:
2324:
2306:
2303:
2297:
2267:
2262:
2237:
2211:
2181:
2125:
2076:Maintainability
2052:
2047:
2017:
2012:
1999:
1973:
1950:
1941:Inception score
1929:
1906:
1884:Computer Vision
1878:
1850:
1787:
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1261:
1259:"Archived copy"
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566:
563:
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536:
530:
525:
512:reproducibility
491:false precision
451:shows that the
409:
407:False precision
403:
398:
397:
396:
393:
390:
381:
378:
367:
366:
363:target grouping
336:
331:
330:
324:justesse de tir
218:reproducibility
171:
103:arithmetic mean
28:
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22:
15:
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2118:
2116:Loose coupling
2113:
2108:
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2098:
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2088:
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2014:
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2011:
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1811:Davies-Bouldin
1808:
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1775:
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1725:Classification
1721:
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1557:External links
1555:
1552:
1551:
1536:
1496:
1481:
1441:
1435:978-0367859459
1434:
1416:
1397:
1378:
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1295:
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1218:Semin Nucl Med
1201:
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843:precision at k
809:
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764:variable error
752:constant error
731:
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551:true negatives
547:true positives
532:Main article:
529:
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516:
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509:
479:
478:
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445:standard error
402:
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361:Accuracy of a
360:
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278:if it is both
170:
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144:(a measure of
126:(a measure of
26:
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4628:ISO standards
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4623:Psychometrics
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4613:Biostatistics
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3737:
3734:
3732:
3729:
3727:
3724:
3722:
3719:
3717:
3714:
3712:
3709:
3707:
3704:
3702:
3699:
3697:
3694:
3692:
3689:
3687:
3684:
3682:
3679:
3677:
3674:
3672:
3669:
3667:
3664:
3662:
3659:
3655:
3652:
3650:
3647:
3646:
3645:
3642:
3640:
3637:
3635:
3632:
3630:
3627:
3625:
3622:
3620:
3617:
3615:
3612:
3610:
3607:
3605:
3602:
3600:
3597:
3594:
3590:
3587:
3585:
3582:
3580:
3577:
3575:
3572:
3570:
3567:
3565:
3562:
3560:
3557:
3555:
3552:
3550:
3547:
3545:
3542:
3540:
3537:
3535:
3532:
3530:
3527:
3525:
3522:
3520:
3517:
3515:
3512:
3510:
3507:
3505:
3502:
3500:
3497:
3495:
3492:
3490:
3487:
3485:
3482:
3480:
3477:
3475:
3472:
3468:
3465:
3463:
3460:
3458:
3455:
3453:
3450:
3448:
3445:
3444:
3443:
3440:
3438:
3435:
3433:
3430:
3428:
3425:
3423:
3420:
3418:
3415:
3413:
3410:
3408:
3405:
3403:
3400:
3398:
3395:
3393:
3390:
3388:
3385:
3384:
3382:
3378:
3372:
3369:
3367:
3364:
3362:
3359:
3357:
3354:
3352:
3349:
3347:
3344:
3342:
3339:
3337:
3334:
3332:
3329:
3327:
3324:
3322:
3319:
3317:
3314:
3312:
3309:
3307:
3304:
3302:
3299:
3297:
3294:
3292:
3289:
3287:
3284:
3282:
3279:
3277:
3274:
3272:
3269:
3267:
3264:
3262:
3259:
3257:
3254:
3252:
3249:
3247:
3244:
3240:
3237:
3235:
3232:
3230:
3227:
3225:
3222:
3220:
3217:
3215:
3212:
3210:
3207:
3205:
3202:
3200:
3197:
3195:
3192:
3190:
3187:
3185:
3182:
3180:
3177:
3175:
3172:
3170:
3167:
3165:
3162:
3160:
3157:
3156:
3155:
3152:
3150:
3147:
3145:
3142:
3140:
3137:
3135:
3132:
3130:
3127:
3125:
3122:
3120:
3117:
3115:
3112:
3110:
3107:
3105:
3102:
3100:
3097:
3095:
3092:
3090:
3087:
3085:
3082:
3080:
3077:
3075:
3072:
3070:
3067:
3065:
3062:
3060:
3057:
3055:
3052:
3050:
3047:
3045:
3042:
3040:
3037:
3035:
3032:
3030:
3027:
3023:
3020:
3019:
3018:
3015:
3013:
3010:
3008:
3005:
3003:
3000:
2998:
2995:
2993:
2990:
2988:
2985:
2983:
2980:
2978:
2975:
2973:
2970:
2968:
2965:
2963:
2960:
2958:
2955:
2953:
2950:
2948:
2945:
2943:
2940:
2938:
2935:
2933:
2930:
2928:
2925:
2923:
2920:
2918:
2915:
2913:
2910:
2908:
2905:
2903:
2900:
2898:
2895:
2893:
2890:
2888:
2885:
2883:
2880:
2878:
2875:
2873:
2870:
2868:
2865:
2863:
2860:
2858:
2855:
2853:
2850:
2848:
2845:
2843:
2840:
2838:
2835:
2833:
2830:
2828:
2825:
2823:
2820:
2818:
2815:
2813:
2810:
2808:
2805:
2801:
2798:
2796:
2793:
2791:
2788:
2787:
2786:
2783:
2781:
2778:
2776:
2773:
2771:
2768:
2766:
2763:
2761:
2758:
2756:
2753:
2751:
2748:
2746:
2743:
2741:
2738:
2736:
2733:
2731:
2728:
2726:
2723:
2721:
2718:
2716:
2713:
2711:
2708:
2706:
2703:
2701:
2698:
2696:
2693:
2691:
2688:
2686:
2683:
2681:
2678:
2676:
2673:
2671:
2668:
2666:
2663:
2661:
2658:
2656:
2653:
2651:
2648:
2646:
2643:
2641:
2638:
2636:
2633:
2631:
2628:
2626:
2623:
2621:
2618:
2616:
2613:
2611:
2608:
2606:
2603:
2601:
2598:
2596:
2593:
2591:
2588:
2586:
2583:
2581:
2578:
2576:
2573:
2571:
2568:
2566:
2563:
2559:
2556:
2554:
2551:
2549:
2546:
2544:
2541:
2539:
2536:
2535:
2534:
2531:
2529:
2526:
2524:
2521:
2519:
2516:
2514:
2511:
2509:
2506:
2504:
2501:
2499:
2496:
2494:
2491:
2489:
2486:
2484:
2481:
2479:
2476:
2474:
2471:
2469:
2466:
2464:
2461:
2459:
2456:
2454:
2451:
2447:
2444:
2442:
2439:
2437:
2434:
2432:
2429:
2427:
2424:
2422:
2419:
2417:
2414:
2412:
2409:
2407:
2404:
2402:
2399:
2397:
2394:
2392:
2389:
2387:
2384:
2383:
2382:
2379:
2377:
2374:
2372:
2369:
2367:
2364:
2362:
2359:
2357:
2354:
2352:
2349:
2347:
2344:
2342:
2339:
2337:
2334:
2333:
2331:
2327:
2323:
2322:IEC standards
2319:
2315:
2314:ISO standards
2309:
2301:
2294:
2289:
2287:
2282:
2280:
2275:
2274:
2271:
2259:
2251:
2244:
2243:
2240:
2234:
2231:
2229:
2226:
2224:
2221:
2220:
2218:
2214:
2208:
2205:
2203:
2200:
2198:
2195:
2194:
2192:
2188:
2178:
2175:
2173:
2170:
2168:
2165:
2163:
2160:
2158:
2155:
2153:
2150:
2148:
2145:
2143:
2140:
2138:
2135:
2134:
2132:
2128:
2122:
2121:Orthogonality
2119:
2117:
2114:
2112:
2109:
2107:
2104:
2102:
2099:
2097:
2094:
2092:
2089:
2087:
2084:
2082:
2079:
2077:
2074:
2072:
2069:
2068:
2066:
2062:
2059:
2055:
2051:
2044:
2039:
2037:
2032:
2030:
2025:
2024:
2021:
2009:
2006:
2005:
2002:
1996:
1993:
1991:
1988:
1986:
1983:
1982:
1980:
1976:
1970:
1967:
1965:
1962:
1961:
1959:
1957:
1953:
1947:
1944:
1942:
1939:
1938:
1936:
1932:
1926:
1923:
1921:
1918:
1917:
1915:
1913:
1909:
1903:
1900:
1898:
1895:
1893:
1890:
1889:
1887:
1885:
1881:
1875:
1872:
1870:
1867:
1865:
1862:
1861:
1859:
1857:
1853:
1847:
1844:
1842:
1839:
1837:
1834:
1832:
1829:
1827:
1826:Jaccard index
1824:
1822:
1819:
1817:
1814:
1812:
1809:
1807:
1804:
1802:
1799:
1798:
1796:
1794:
1790:
1784:
1781:
1779:
1776:
1774:
1771:
1769:
1766:
1764:
1761:
1759:
1756:
1754:
1751:
1749:
1746:
1744:
1741:
1739:
1736:
1734:
1731:
1730:
1728:
1726:
1722:
1716:
1713:
1711:
1708:
1706:
1703:
1701:
1698:
1696:
1693:
1691:
1688:
1686:
1683:
1681:
1678:
1676:
1673:
1671:
1668:
1666:
1663:
1662:
1660:
1658:
1654:
1649:
1642:
1637:
1635:
1630:
1628:
1623:
1622:
1619:
1612:
1608:
1605:
1602:
1599:
1597:
1594:
1592:
1588:
1585:
1583:
1580:
1577:
1573:
1570:
1568:
1564:
1561:
1560:
1547:
1543:
1539:
1533:
1529:
1525:
1520:
1515:
1511:
1507:
1500:
1492:
1488:
1484:
1478:
1474:
1470:
1465:
1460:
1456:
1452:
1445:
1437:
1431:
1427:
1420:
1412:
1408:
1401:
1393:
1389:
1382:
1367:
1363:
1357:
1338:
1334:
1327:
1320:
1311:
1306:
1299:
1291:
1285:
1271:on 2015-03-11
1267:
1260:
1254:
1243:
1239:
1235:
1231:
1227:
1224:(4): 283–98.
1223:
1219:
1212:
1205:
1197:
1193:
1189:
1185:
1180:
1175:
1171:
1167:
1163:
1156:
1148:
1144:
1140:
1134:
1130:
1129:
1121:
1115:
1109:
1100:
1091:
1083:
1081:0-935702-75-X
1077:
1073:
1072:
1064:
1057:
1052:
1050:
1048:
1039:
1035:
1031:
1027:
1023:
1019:
1012:
1003:
1001:
996:
985:
982:
980:
977:
975:
972:
970:
967:
965:
962:
960:
957:
955:
952:
950:
947:
945:
942:
939:
935:
932:
930:
927:
925:
922:
920:
917:
915:
912:
910:
907:
905:
902:
899:
896:
895:
888:
886:
881:
873:
865:
861:
850:
848:
844:
838:
835:
831:
827:
823:
819:
815:
805:
802:
800:
797:
793:
789:
780:
771:
769:
765:
761:
757:
753:
749:
745:
741:
740:psychophysics
737:
736:psychometrics
727:
725:
720:
715:
691:
672:
670:
666:
665:
658:
626:
623:
620:
617:
614:
611:
608:
605:
602:
599:
596:
591:
588:
585:
582:
579:
573:
560:
556:
552:
548:
544:
540:
535:
520:
513:
510:
507:
506:repeatability
504:
503:
502:
499:
495:
492:
486:
484:
476:
473:
469:
465:
461:
460:
459:
456:
454:
450:
446:
441:
439:
435:
431:
427:
423:
419:
413:
408:
388:
383:
376:
371:
370:
364:
357:
355:
350:
348:
343:
341:
327:
325:
320:
318:
313:
311:
306:
303:
301:
297:
293:
289:
285:
281:
277:
272:
269:
265:
260:
258:
257:
252:
251:
246:
243:The field of
241:
239:
235:
231:
227:
223:
222:repeatability
219:
215:
211:
207:
203:
199:
194:
192:
188:
184:
180:
176:
163:
158:
155:
151:
147:
143:
142:
137:
136:
135:
133:
129:
125:
124:
123:random errors
119:
110:
106:
104:
100:
99:
94:
89:
87:
86:
81:
80:
75:
71:
67:
66:
61:
60:
55:
48:
44:
40:
36:
32:
19:
4440:27000 series
2891:
2197:ISO/IEC 9126
2156:
2147:Adaptability
1742:
1590:
1575:
1566:
1509:
1499:
1454:
1444:
1425:
1419:
1410:
1406:
1400:
1391:
1387:
1381:
1369:. Retrieved
1366:scikit-learn
1365:
1356:
1344:. Retrieved
1332:
1319:
1298:
1273:. Retrieved
1266:the original
1253:
1221:
1217:
1204:
1172:(1): 39–59.
1169:
1165:
1155:
1127:
1120:
1113:
1108:
1099:
1090:
1070:
1063:
1024:(1): 45–47.
1021:
1017:
1011:
909:Data quality
871:
863:
860:DIKW Pyramid
856:
839:
834:ground truth
811:
803:
785:
763:
755:
751:
743:
733:
716:
678:
662:
659:
538:
537:
517:
511:
505:
500:
496:
487:
480:
457:
442:
436:such as the
429:
414:
410:
351:
344:
337:
323:
321:
314:
307:
304:
299:
287:
283:
279:
275:
273:
261:
254:
248:
242:
195:
186:
178:
172:
161:
153:
139:
131:
121:
117:
115:
97:
96:
90:
84:
83:
77:
74:observations
70:measurements
64:
63:
57:
53:
52:
47:reproducible
38:
34:
4228:20000–29999
3380:10000–19999
2152:Correctness
2142:Reliability
2106:Testability
2101:Scalability
2096:Readability
2091:Reusability
2086:Portability
2081:Flexibility
1346:11 December
964:Probability
760:reliability
742:, the term
472:calibration
268:sample size
256:variability
206:measurement
202:engineering
4602:Categories
4007:16949 (TS)
3604:11941 (TR)
2302:standards
2167:Robustness
2162:Efficiency
1978:Similarity
1920:Perplexity
1831:Rand index
1816:Dunn index
1801:Silhouette
1793:Clustering
1657:Regression
1519:2211.06477
1464:2211.06479
1394:: 281–306.
1310:1503.06410
1275:2015-08-09
1166:Ergonomics
1147:1246529321
991:References
796:transistor
559:Rand index
420:reference
405:See also:
245:statistics
234:colloquial
230:synonymous
79:true value
4618:Metrology
3962:15926 WIP
3326:9592/9593
3251:9000/9001
3139:8805/8806
2216:Processes
2137:Usability
2057:Qualities
1748:Precision
1700:RMSE/RMSD
1491:195891648
1188:0014-0139
1038:0949-1775
936:(section
814:databases
756:Precision
669:see below
664:precision
418:traceable
347:measurand
189:if their
181:if their
118:precision
85:Precision
39:precision
4587:Category
2312:List of
2172:Security
2157:Accuracy
2130:External
2064:Internal
1964:Coverage
1743:Accuracy
1578:magazine
1546:51603737
1337:Archived
1284:cite web
1242:Archived
1196:34006206
891:See also
748:validity
744:accuracy
724:ImageNet
688:Accuracy
570:Accuracy
539:Accuracy
422:standard
280:accurate
210:quantity
179:accurate
162:accuracy
154:trueness
132:accuracy
98:trueness
65:Accuracy
43:repeated
35:Accuracy
18:Accuracy
4488:29199-2
4360:23094-2
4355:23094-1
4345:23090-3
4214:19794-5
4209:19775-1
3997:16612-2
3987:16355-1
3676:13406-2
3634:12234-2
3402:10118-3
2258:Commons
1856:Ranking
1846:SimHash
1733:F-score
929:F-score
284:precise
226:results
198:science
187:precise
183:average
4502:30000+
3341:9797-1
3149:8820-5
3094:8501-1
2650:1073-2
2645:1073-1
2329:1–9999
2177:Safety
1753:Recall
1544:
1534:
1489:
1479:
1432:
1371:17 May
1238:112681
1236:
1194:
1186:
1145:
1135:
1078:
1036:
642:where
298:) and
292:random
116:While
4569:80000
4564:56000
4559:55000
4554:50001
4549:45001
4544:42010
4539:40500
4534:39075
4529:38500
4524:37001
4519:32000
4514:31000
4509:30170
4493:29500
4483:29148
4479:29110
4475:28000
4470:27729
4465:27006
4460:27005
4455:27002
4450:27001
4445:27000
4435:26324
4430:26300
4425:26262
4420:26000
4415:25964
4410:25178
4405:24728
4400:24707
4395:24617
4390:24613
4385:24517
4380:23941
4375:23360
4370:23271
4365:23270
4350:23092
4340:23009
4335:23008
4330:23003
4325:23000
4320:22537
4315:22395
4310:22301
4305:22300
4300:22275
4295:22000
4290:21827
4285:21500
4280:21122
4275:21047
4270:21001
4265:21000
4260:20830
4255:20802
4250:20400
4245:20121
4240:20022
4235:20000
4219:19831
4204:19770
4199:19757
4194:19752
4189:19600
4184:19510
4179:19509
4174:19508
4169:19507
4164:19506
4159:19505
4154:19503
4149:19502
4144:19501
4139:19500
4134:19439
4129:19407
4124:19136
4119:19125
4114:19115
4109:19114
4092:19092
4087:19011
4082:19005
4077:18916
4072:18629
4067:18245
4062:18181
4057:18014
4052:18004
4047:17799
4042:17506
4037:17442
4032:17369
4027:17203
4022:17100
4017:17025
4012:17024
4002:16750
3992:16485
3982:16262
3977:16023
3972:15938
3967:15930
3957:15926
3952:15924
3947:15919
3942:15897
3937:15707
3925:15706
3920:15693
3915:15686
3910:15511
3905:15504
3900:15438
3895:15445
3878:15444
3873:15408
3868:15398
3863:15291
3858:15288
3853:15189
3848:15022
3843:14971
3838:14882
3833:14764
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