Abstract: A system for generating algorithmic models comprising a function module to generate a desirability function, an automated machine learning module, and a UI module. The desirability function defines a single desirability value based on an algorithmic model accuracy criteria, criteria for algorithmic model quality, criteria for model fidelity, and criteria for the benefits and cost of model deployment. Specific hard and soft constraints regarding these and other user-defined criteria can also be specified by the user. The automated machine learning module generates an algorithmic model by training the algorithmic model against a model data set, identifying the model with the greatest desirability with respect to all criteria as combined via the desirability function. The UI module generates a user interface to display the overall desirability as well as all model criteria configured by the user. The displayed criteria and desirability are selectable and definable.

Abstract: An apparatus comprising feature engineering and text explanation modules for explaining text from predictive results of an algorithmic model. The feature engineering module creates vectors for string variables, each string variable comprising identified text, each vector created comprising a numeric combination, each numeric combination identifying a variable name and a value having a word or a phrase. The feature engineering module causes a predictive engine to generate predictive results using the algorithmic model, the data set, and the vectors created. The predictive results comprising the string variable or a modified version of the string variable and a confidence score. The text explanation module maps words and phrases from qualified text of the string variable, or modified version, to the numeric combinations of the vectors and determines a probability score for each word and each phrase. The most influential words and phrases are plotted on a chart.

Abstract: A predictive engine for interpreting data structures that includes an interpreter and visualization generator. The interpreter identifies a relational pattern between target feature variables and other feature variables based on recognizing a variable dependency between the target feature data and the other feature data and generate at least one meta-data feature set and associated result metrics. The visualization generator can recommend at least one visualization based on the at least one meta-data feature set and the associated result metrics. The interpreter includes multiple stages that perform variable selection, interaction detection, and pattern discovery and ranking. The predictive engine also includes a data preparer configured to sort, categorize, and filter the data structures according to at least one of data type, hierarchical data structures, unique values, missing values and date/time data.