User interface based variable machine modeling

Patent metadata

Field	Value
Publication number	US-10552002-B1
Application number	US-201715655408-A
Country	US
Kind code	B1
Filing date	Jul 20, 2017
Priority date	Sep 27, 2016
Publication date	Feb 4, 2020
Grant date	Feb 4, 2020

Abstract

In various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. In response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. The comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.

First claim

What is claimed is:

1. A method, comprising:
causing, by one or more processors of a machine, presentation of a graphical user interface having a set of selectable graphical interface elements including a first graphical interface element representing a set of data sets, a second graphical interface element representing a set of transform families, and a third graphical interface element representing a set of model families, each transform family comprising a particular transform scheme for transforming one or more values of a data set from one form to another form, each model family comprising a family identification and a set of code for a particular machine-learning algorithm that generates a particular machine-learning model for one or more values;
receiving, by the one or more processors of a machine, a selection of a particular data set through the graphical user interface, the particular data set including a set of values associated with a set of identifiers;
receiving, by the one or more processors of the machine, a selection of a transform scheme through the graphical user interface, the transform scheme configured to transform one or more values of the particular data set from a first form to a second form;
receiving, by the one or more processors of the machine, a selection of a first machine-learning algorithm and a second machine-learning algorithm through the graphical user interface, the first machine-learning algorithm configured to generate a first machine-learning model for the set of values and the second machine-learning algorithm configured to generate a second machine-learning model for the set of values;
in response to selection of the first machine-learning algorithm and the second machine-learning algorithm, determining a first iteration value for each given first parameter of two or more first parameters within the first machine-learning algorithm, and determining a second iteration value for each given second parameter of two or more second parameters within the second machine-learning algorithm;
iteratively executing, by the one or more processors of the machine, the first machine-learning algorithm, using the two or more first parameters, according to a first iteration order and the first iteration value to process the set of values and generate a plurality of first machine-learning models;
iteratively executing, by the one or more processors of the machine, the second machine-learning algorithm, using the two or more second parameters, according to a second iteration order and the second iteration value to process the set of values and generate a plurality of second machine-learning models;
determining, by the one or more processors of the machine, one or more comparison metric values for data output by each of the plurality of first machine-learning models and the plurality of second machine-learning models; and
causing presentation, by the one or more processors of the machine, of the comparison metric values for the data output by the plurality of first machine-learning models and the plurality of second machine-learning models, the presentation comprising a selectable user interface element configured to cause the presentation of a result of at least one of a first machine learning model or a second machine learning model, the result comprising at least one of the comparison metric values.

2. The method of claim 1 , further comprising:
determining a first iteration order for the two or more first parameters within the first machine-learning algorithm and a second iteration order for the two or more second parameters within the second machine-learning algorithm.

3. The method of claim 1 , wherein iteratively modeling the set of values according to the first machine-learning algorithm further comprises:
generating a first set of machine-learning models for the set of values, each machine-learning model of the first set of machine-learning models corresponding to a different first iteration value of each first parameter of the two or more first parameters within the first machine-learning algorithm.

4. The method of claim 1 , wherein iteratively modeling the set of values according to the second machine-learning algorithm further comprises:
generating a second set of machine-learning models for the set of values, each machine-learning model of the second set of machine-learning models corresponding to a different second iteration value of each second parameter of the two or more second parameters within the second machine-learning algorithm.

5. The method of claim 1 , wherein the set of model families comprises a predetermined set of model families, the method further comprising:
receiving an additional model family including another family identification and another set of code for an additional machine-learning algorithm for generating another model for the set of values;
incorporating the additional model family into the set of model families; and
generating a selectable graphical interface element for the additional model family within the third graphical interface element.

6. A computer implemented system, comprising:
one or more processors; and
a processor-readable storage device comprising processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
causing presentation of a graphical user interface having a set of selectable graphical interface elements including a first graphical interface element representing a set of data sets, a second graphical interface element representing a set of transform families, and a third graphical interface element representing a set of model families, each transform family comprising a particular transform scheme for transforming one or more values of a data set from one form to another form, each model family comprising a family identification and a set of code for a particular machine-learning algorithm that generates a particular machine-learning model for one or more values;
receiving a selection of a particular data set through the graphical user interface, the particular data set including a set of values associated with a set of identifiers;
receiving a selection of a transform scheme through the graphical user interface, the transform scheme configured to transform one or more values of the particular data set from a first form to a second form;
receiving a selection of a first machine-learning algorithm and a second machine-learning algorithm through the graphical user interface, the first machine-learning algorithm configured to generate a first machine-learning model for the set of values and the second machine-learning algorithm configured to generate a second machine-learning model for the set of values;
in response to selection of the first machine-learning algorithm and the second machine-learning algorithm, determining a first iteration value for each given first parameter of two or more first parameters within the first machine-learning algorithm, and determining a second iteration value for each given second parameter of two or more second parameters;
iteratively executing the first machine-learning algorithm, using the two or more first parameters, according to a first iteration order and the first iteration value to process the set of values and generate a plurality of first machine-learning models;
iteratively executing the second machine-learning algorithm, using the two or more second parameters, according to a second iteration order and the second iteration value to process the set of values and generate a plurality of second machine-learning models;
determining one or more comparison metric values for data output by each of the plurality of first machine-learning models and the plurality of seco

Assignees

• Palantir Technologies Inc

Inventors

• Maclean Matthew
• Duffield Benjamin
• Elliot Mark

Classifications

• G06F3/0482 Primary

  Interaction with lists of selectable items, e.g. menus · CPC title

• G06F3/04847

  Interaction techniques to control parameter settings, e.g. interaction with sliders or dials · CPC title

• G06F3/04817

  using icons (graphical or visual programming using iconic symbols G06F8/34) · CPC title

• G06N20/00

  Machine learning · CPC title