Our concerns apply to the inadequate ways statistical distributions of crystallographic orientations are compared and occasionally confirmed to agree sufficiently well.The authors of“Machine learning enhanced analysi...Our concerns apply to the inadequate ways statistical distributions of crystallographic orientations are compared and occasionally confirmed to agree sufficiently well.The authors of“Machine learning enhanced analysis of EBSD data for texture representation”1 suggest a method to replace an EBSD dataset of crystallographic orientations with a much smaller synthetic dataset preserving the texture.They claim that their“texture adaptive clustering and sampling”algorithm generates datasets of a few hundred crystallographic orientations,realizing an equivalent crystallographic orientation distribution as the initial dataset.To prove the principle and substantiate their claim of equivalent orientation distributions,the authors content themselves with(i)a visual inspection of the crystallographic pole density function,in fact,of three crystallographic“pole figures”and(ii)Kolmogorov–Smirnov tests for each of the three Euler angles of the crystallographic orientations individually.However,these criteria are insufficient to confirm equivalence of orientation distributions,they do not provide scientific evidence to substantiate the authors’claim that“texture adaptive clustering and sampling”generates crystallographic orientations in terms of their Euler angles representing the same texture.展开更多
The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes {...The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes {0,1} classification of T. A special case of logistic regression called weights-of-evidence （WofE） is geolo- gists＇ favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regres- sion view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence （BoostWofE） was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the （initial） purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to ＂validate＂ this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking condi- tional independence whatever the consecutively proces- sing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly com- pensate violations of joint conditional independence if the predictors are indicators.展开更多
文摘Our concerns apply to the inadequate ways statistical distributions of crystallographic orientations are compared and occasionally confirmed to agree sufficiently well.The authors of“Machine learning enhanced analysis of EBSD data for texture representation”1 suggest a method to replace an EBSD dataset of crystallographic orientations with a much smaller synthetic dataset preserving the texture.They claim that their“texture adaptive clustering and sampling”algorithm generates datasets of a few hundred crystallographic orientations,realizing an equivalent crystallographic orientation distribution as the initial dataset.To prove the principle and substantiate their claim of equivalent orientation distributions,the authors content themselves with(i)a visual inspection of the crystallographic pole density function,in fact,of three crystallographic“pole figures”and(ii)Kolmogorov–Smirnov tests for each of the three Euler angles of the crystallographic orientations individually.However,these criteria are insufficient to confirm equivalence of orientation distributions,they do not provide scientific evidence to substantiate the authors’claim that“texture adaptive clustering and sampling”generates crystallographic orientations in terms of their Euler angles representing the same texture.
文摘The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes {0,1} classification of T. A special case of logistic regression called weights-of-evidence （WofE） is geolo- gists＇ favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regres- sion view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence （BoostWofE） was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the （initial） purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to ＂validate＂ this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking condi- tional independence whatever the consecutively proces- sing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly com- pensate violations of joint conditional independence if the predictors are indicators.
