Systems and techniques for predictive data analytics

Patent metadata

Field	Value
Publication number	US-9659254-B2
Application number	US-201615217640-A
Country	US
Kind code	B2
Filing date	Jul 22, 2016
Priority date	May 23, 2014
Publication date	May 23, 2017
Grant date	May 23, 2017

Abstract

Systems and techniques for predictive data analytics are described. In a method for selecting a predictive model for a prediction problem, the suitabilities of predictive modeling procedures for the prediction problem may be determined based on characteristics of the prediction problem and/or on attributes of the respective modeling procedures. A subset of the predictive modeling procedures may be selected based on the determined suitabilities of the selected modeling procedures for the prediction problem. A resource allocation schedule allocating computational resources for execution of the selected modeling procedures may be generated, based on the determined suitabilities of the selected modeling procedures for the prediction problem. Results of the execution of the selected modeling procedures in accordance with the resource allocation schedule may be obtained. A predictive model for the prediction problem may be selected based on those results.

First claim

What is claimed is:

1. A method for selecting a predictive model for a prediction problem, the method comprising:
determining suitabilities of a plurality of predictive modeling procedures for the prediction problem based, at least in part, on characteristics of the prediction problem and/or on attributes of the respective modeling procedures, wherein, for each of the predictive modeling procedures, the determined suitability of the predictive modeling procedure for the prediction problem comprises a value representing an estimated performance of a predictive model generated using the predictive modeling procedure and applied to the prediction problem;
selecting at least a subset of the predictive modeling procedures based, at least in part, on the determined suitabilities of the selected modeling procedures for the prediction problem;
transmitting instructions to a plurality of processing nodes, the instructions comprising a resource allocation schedule allocating resources of the processing nodes for execution of the selected modeling procedures based, at least in part, on the determined suitabilities of the selected modeling procedures for the prediction problem, wherein the resource allocation schedule allocates different portions of the resources of the processing nodes to different subsets of the selected modeling procedures in accordance with the determined suitabilities of the selected modeling procedures;
receiving results of the execution of the selected modeling procedures by the plurality of processing nodes in accordance with the resource allocation schedule, wherein the results include predictive models generated by the selected modeling procedures, and/or scores of the models for data associated with the prediction problem; and
selecting, from the predictive models, a predictive model for the prediction problem based, at least in part, on the score of the selected predictive model.

2. The method of claim 1 , wherein determining the suitabilities of the plurality of predictive modeling procedures for the prediction problem comprises:
eliminating at least one predictive modeling procedure from consideration based on one or more relationships between the characteristics of the prediction problem and the attributes of the eliminated procedure, and/or
for at least one predictive modeling procedure, determining the value representing the estimated performance of a predictive model generated using the predictive modeling procedure based on one or more relationships between the characteristics of the prediction problem and the attributes of the predictive modeling procedure.

3. The method of claim 1 , wherein selecting at least a subset of the predictive modeling procedures comprises:
selecting one or more predictive modeling procedures for which the value exceeds a threshold suitability value,
selecting one or more predictive modeling procedures for which the value is within a specified range of a highest value assigned to any of the predictive modeling procedures for the prediction problem, or
selecting a specified fraction of the predictive modeling procedures for which the value is highest.

4. The method of claim 3 , further comprising determining the threshold suitability value based, at least in part, on an amount of processing resources available for execution of the selected modeling procedures.

5. The method of claim 1 , wherein the allocated resources of the processing nodes comprise execution cycles of the processing nodes, execution time on the processing nodes, and/or computer-readable storage of the processing nodes.

6. The method of claim 1 , further comprising receiving budget data indicating a temporal and/or resource budget for executing the selected modeling procedures, wherein the resource allocation schedule allocates resources of the processing nodes in accordance with the temporal and/or resource budget.

7. The method of claim 1 , wherein the results of execution of the selected predictive modeling procedures include results of: fitting the predictive models to a first portion of the data associated with the prediction problem, and testing the fitted models on a second portion of the data associated with the prediction problem.

8. The method of claim 7 , wherein fitting the predictive models to the first portion of the data comprises tuning one or more parameters of the selected modeling procedures and/or one or more parameters of the predictive models.

9. The method of claim 1 , wherein selecting a predictive model for the prediction problem based, at least in part, on the score of the selected model comprises: selecting a predictive model having a score that exceeds a threshold score, or selecting a predictive model having a score within a specified range of a highest score of any of the predictive models.

10. The method of claim 1 , further comprising: generating a blended predictive model by combining two or more of the predictive models; and evaluating the blended predictive model.

11. The method of claim 1 , further comprising: iteratively receiving the scores of the predictive models and re-determining the suitabilities of the selected predictive modeling procedures for the prediction problem based, at least in part, on the scores until a temporal and/or resource budget has been used or a score of at least one of the predictive models exceeds a threshold score.

12. A predictive modeling apparatus comprising:
a memory configured to store processor-executable instructions; and
a processor configured to execute the processor-executable instructions, wherein executing the processor-executable instructions causes the apparatus to perform a method comprising:
determining suitabilities of a plurality of predictive modeling procedures for a prediction problem based, at least in part, on characteristics of the prediction problem and/or on attributes of the respective modeling procedures, wherein, for each of the predictive modeling procedures, the determined suitability of the predictive modeling procedure for the prediction problem comprises a value representing an estimated performance of a predictive model generated using the predictive modeling procedure and applied to the prediction problem,
selecting at least a subset of the predictive modeling procedures based, at least in part, on the determined suitabilities of the selected modeling procedures for the prediction problem,
transmitting instructions to a plurality of processing nodes, the instructions comprising a resource allocation schedule allocating resources of the processing nodes for execution of the selected modeling procedures based, at least in part, on the determined suitabilities of the selected modeling procedures for the prediction problem, wherein the resource allocation schedule allocates different portions of the resources of the processing nodes to different subsets of the selected modeling procedures in accordance with the determined suitabilities of the selected modeling procedures,
receiving results of the execution of the selected modeling procedures by the plurality of processing nodes in accordance with the resource allocation schedule, wherein the results include predictive models generated by the selected modeling procedures, and/or scores of the models for data associated with the prediction problem, and
selecting, from the predictive models, a predictive model for the prediction problem based, at least in part, on the score of the selected predictive model.

13. The apparatus of claim 12 , wherein determining the suitabilities of the plurality of predictive modeling procedures for the prediction problem comprises:
eliminating at least one p

Assignees

• Datarobot Inc

Inventors

• Achin Jeremy
• Degodoy Thomas
• Owen Timothy
• Conort Xavier

Classifications

• G06N5/02

  Knowledge representation; Symbolic representation · CPC title

• G06Q10/04 Primary

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• G06F9/5011

  the resources being hardware resources other than CPUs, Servers and Terminals · CPC title

• G06N5/04 Primary

  Inference or reasoning models · CPC title

• G06N99/005

  Physics · mapped topic