User interface based variable machine modeling
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.
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 model families;
receiving, by the one or more processors of the machine, a selection of a particular data set through the graphical user interface, the particular data set including a set of values;
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; and
in response to selection of the first machine-learning algorithm and the second machine-learning algorithm:
iteratively executing, by the one or more processors of the machine, the first machine-learning algorithm, using a first iteration order to process the set of values of the particular data set and generate a plurality of first machine-learning models, the first iteration order determined based on a first set of upper and lower bound values and a first step value indicating an order of iterations occurring between the first set of upper and lower bound values for the first machine-learning algorithm;
iteratively executing, by the one or more processors of the machine, the second machine-learning algorithm, using a second iteration order to process the set of values of the particular data set and generate a plurality of second machine-learning models the second iteration order determined based on a second set of upper and lower bound values and a second step value indicating an order of iterations occurring between the second set of upper and lower bound values for the second machine-learning algorithm;
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.
2. The method of claim 1 , wherein the presentation of the comparison metric values further comprises 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.
3. The method of claim 1 , wherein executing 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 two or more first parameters within the first machine-learning algorithm.
4. The method of claim 1 , wherein executing 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 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 second graphical interface element.
6. A computer implemented system, comprising:
one or more processors; and
a machine-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, 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 model families,
receiving, by the one or more processors of the machine, a selection of a particular data set through the graphical user interface, the particular data set including a set of values;
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; and
in response to selection of the first machine-learning algorithm and the second machine-learning algorithm:
iteratively executing, by the one or more processors of the machine, the first machine-learning algorithm, using a first iteration order to process the set of values of the particular data set and generate a plurality of first machine-learning models, the first iteration order determined based on a first set of upper and lower bound values and a first step value indicating an order of iterations occurring between the first set of upper and lower bound values for the first machine-learning algorithm;
iteratively executing, by the one or more processors of the machine, the second machine-learning algorithm, using a second iteration order to process the set of values of the particular data set and generate a plurality of second machine-learning models, the second iteration order determined based on a second set of upper and lower bound values and a second step value indicating an order of iterations occurring between the second set of upper and lower bound values for the second machine-learning algorithm;
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.
7. The system of claim 6 , wherein the presentation of the comparison metric values further comprises 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.
8. The system of claim 6 , wherein executing 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 two or more first parameters within the first machine-learning algorithm.
9. The system of claim 6 , wherein executing 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 two or more second parameters within the second machine-learning algorithm.
10. The system of claim 6 , wherein the set of model families comprises a predetermined set of model families, the operations 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 second graphical interface element.
11. A non-transitory machine-readable storage device comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations 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 model families,
receiving, by the one or more processors of the machine, a selection of a particular data set through the graphical user interface, the particular data set including a set of values;
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; and
in response to selection of the first machine-learning algorithm and the second machine-learning algorithm:
iteratively executing, by the one or more processors of the machine, the first machine-learning algorithm, using a first iteration order to process the set of values of the particular data set and generate a plurality of first machine-learning models, the first iteration order determined based on a first set of upper and lower bound values and a first step value indicating an order of iterations occurring between the first set of upper and lower bound values for the first machine-learning algorithm;
iteratively executing, by the one or more processors of the machine, the second machine-learning algorithm, using a second iteration order to process the set of values of the particular data set and generate a plurality of second machine-learning models, the second iteration order determined based on a second set of upper and lower bound values and a second step value indicating an order of iterations occurring between the second set of upper and lower bound values for the second machine-learning algorithm;
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.
12. The non-transitory machine-readable storage device of claim 11 , wherein the presentation of the comparison metric values further comprises 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.
13. The non-transitory machine-readable storage device of claim 11 wherein executing 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 two or more first parameters within the first machine-learning algorithm.
14. The non-transitory machine-readable storage device of claim 11 , wherein executing 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 two or more second parameters within the second machine-learning algorithm.
15. The non-transitory machine-readable storage device of claim 11 , wherein the set of model families comprises a predetermined set of model families, the operations 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 second graphical interface element.