1. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to: compute a baseline penalty value using a plurality of observation vectors, wherein each observation vector of the plurality of observation vectors includes an explanatory variable value and a response variable value, wherein the baseline penalty value is inversely proportional to a square of a maximum explanatory variable value; compute a set of penalty values based on the computed baseline penalty value; for each penalty value of the set of penalty values, train a classification type model using the respective penalty value and the plurality of observation vectors to compute parameters that define a trained model, wherein the classification type model is trained to predict the response variable value of each observation vector based on the respective explanatory variable value of each observation vector; validate the trained classification type model using the respective penalty value and the plurality of observation vectors to compute a validation criterion value for the trained classification type model that quantifies a validation error; and store the computed validation criterion value, the respective penalty value, and the computed parameters that define a trained model to the computer-readable medium; determine a best classification model based on the stored, computed validation criterion value of each trained classification type model; and output the respective penalty value and the computed parameters associated with the determined best classification model for predicting a new response variable value from a new observation vector.