Abstract: A method of training a machine learning regression model includes defining a prediction accuracy grading function, the prediction accuracy grading function being a many-to-one function that maps prediction accuracies to proxies, each of the prediction accuracies being derivable from a respective prediction of the model and a corresponding actual. The method may further include receiving a plurality of proxies corresponding respectively to a plurality of predictions of the model and, for each of the plurality of proxies, deriving a corresponding approximated actual according to the prediction accuracy grading function. The method may further include calculating an approximated residual for each of the plurality of predictions of the model based on the corresponding approximated actual and adjusting the model based on the approximated residuals.

Abstract: Systems, methods, and apparatuses are presented for a novel natural language tokenizer and tagger. In some embodiments, a method for tokenizing text for natural language processing comprises: generating from a pool of documents, a set of statistical models comprising one or more entries each indicating a likelihood of appearance of a character/letter sequence in the pool of documents; receiving a set of rules comprising rules that identify character/letter sequences as valid tokens; transforming one or more entries in the statistical models into new rules that are added to the set of rules when the entries indicate a high likelihood; receiving a document to be processed; dividing the document to be processed into tokens based on the set of statistical models and the set of rules, wherein the statistical models are applied where the rules fail to unambiguously tokenize the document; and outputting the divided tokens for natural language processing.