J Chem Inf Model - Introducing conformal prediction in predictive modeling. A transparent and flexible alternative to applicability domain determination.

Tópicos

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{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
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{ first(2504) two(1366) second(1323) }
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{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
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{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets.

Resumo Limpo

conform predict introduc altern approach domain applic estim advantag use conform predict follow first approach base consist welldefin mathemat framework second understand confid level concept conform predict straightforward eg confid level mean conform predictor will commit error ie true valu outsid assign predict rang third confid level can vari depend situat model appli consequ chang readili understand ie predict rang increas decreas chang can immedi inspect demonstr use conform predict appli public avail data set

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