Abstract: The computing device transforms lab data and field data into a first format suitable for execution with a supervised machine learning model to determine an input variable importance for a first set of input variables in predicting a field outcome. Based on the determination, the computing device generates one or more logical rules of decision metrics, selects the one or more input variables that yields a higher input variable importance, and generates one or more pass-fail indicators. The computing device combines the one or more pass-fail indicators and generates one or more prediction factor rules. The computing device transforms the field data and the one or more prediction factor rules into a second format suitable for execution with a model to determine a treatment effect for the one or more prediction factor rules. The computing device selects the prediction factor rule that maximizes the treatment effect.

Abstract: The computing device transforms lab data and field data into a first format suitable for execution with a supervised machine learning model to determine an input variable importance for a first set of input variables in predicting a field outcome, generates one or more logical rules of decision metrics, selects the one or more input variables that yields a higher input variable importance, generates one or more pass-fail indicators, combines the one or more pass-fail indicators generates one or more prediction factor rules, transforms the field data and the one or more prediction factor rules into a second format suitable for execution with a model to determine a treatment effect for the one or more prediction factor rules, and selects the prediction factor rule that maximizes the treatment effect of predicting the field outcome of a performance of compounds or biological actives within a range of uncertainty.