Mark Christopher Steadman

20200257992, Aug 13, 2020

Nov 13, 2019

16/682813

- Boston MA, US
Thomas DeGodoy - Medford MA, US
Xavier Conort - Singapore, SG
Mark L. Steadman - Watertown MA, US
Peter Prettenhofer - Wien, AT

G06N 5/04
G06N 20/20
G06N 20/10
G06N 20/00
G06N 5/02
G06F 9/50
G06Q 10/04
G06Q 10/06

A predictive modeling method may include determining a time interval of time-series data; identifying one or more variables of the data as targets; determining a forecast range and a skip range associated with a prediction problem represented by the data; generating training data and testing data from the time-series data; fitting a predictive model to the training data; and testing the fitted model on the testing data. The forecast range may indicate a duration of a period for which values of the targets are to be predicted. The skip range may indicate a temporal lag between the time period corresponding to the data used to make predictions and the time period corresponding to the predictions. The skip range may separate input data subsets representing model inputs from subsets representing model outputs, and separate test data subsets representing model inputs from subsets representing validation data.

20200090075, Mar 19, 2020

Jun 20, 2019

16/447924

- Boston MA, US
Thomas DeGodoy - Medford MA, US
Xavier Conort - Singapore, SG
Sergey Yurgenson - Marlborough MA, US
Mark L. Steadman - Watertown MA, US
Glen Koundry - Waltham MA, US

DataRobot, Inc. - Boston MA

G06N 20/00
G06Q 10/04
G06Q 10/06
G06Q 30/02

A method for determining the predictive value of a feature may include: (a) performing predictive modeling procedures associated with respective predictive models, wherein performing each modeling procedure includes fitting the associated model to an initial dataset representing an initial prediction problem; (b) determining a first accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the initial prediction problem; (c) shuffling values of a feature across observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the modified prediction problem; and (e) determining a model-specific predictive value of the feature for each of the fitted models based on the first and second accuracy scores of the fitted model.