Abstract: An invention in the form of a Consistent Reduced Error Logistic Regression (RELR) Machine method is detailed. This invention includes mechanisms to result in logically consistent, explicit and more reliable learning within the RELR method related to ordinal target outcomes.

Abstract: A machine classification learning method titled Generalized Reduced Error Logistic Regression (RELR) is presented. The method overcomes significant limitations in prior art logistic regression and other machine classification learning methods. The method is applicable to all current applications of logistic regression, but has significantly greater accuracy using smaller sample sizes and larger numbers of input variables than other machine classification learning methods including prior art logistic regression.

Abstract: A machine classification learning method titled Generalized Reduced Error Logistic Regression (RELR) is presented. The method overcomes significant limitations in prior art logistic regression and other machine classification learning methods. The method is applicable to all current applications of logistic regression, but has significantly greater accuracy using smaller sample sizes and larger numbers of input variables than other machine classification learning methods including prior art logistic regression.

Abstract: A machine classification learning method titled Generalized Reduced Error Logistic Regression (RELR) is presented. The method overcomes significant limitations in prior art logistic regression and other machine classification learning methods. The method is applicable to all current applications of logistic regression, but has significantly greater accuracy using smaller sample sizes and larger numbers of input variables than other machine classification learning methods including prior art logistic regression.

Abstract: The present disclosure is directed to a method for Generalized Reduced Error Logistic Regression (Generalized RELR). The method overcomes significant limitations in prior art logistic regression and non-generalized Reduced Error Logistic Regression (RELR) methods. The method is applicable to all current applications of logistic regression, but has significantly greater reliability and validity, using smaller sample sizes and large numbers of input variables, than prior art logistic regression methods. Further, unlike non-generalized RELR, the method of the present invention is not biased by the number of non-missing observations in independent variables. Rather, the method of the invention applies to repeated measures and multilevel designs. This Generalized RELR method also optimally scales solutions to achieve significantly greater accuracy than non-generalized RELR. This Generalized RELR method also automates variable selection to arrive at models with an optimal selection of variables.