User interface based variable machine modeling

1 . A method, comprising:

causing presentation of a graphical user interface having one or more selectable graphical interface elements representing a set of data sets and a set of model families, the set of data sets including a particular data set including a set of values, and the set of model families including a plurality of machine-learning algorithms;

receiving a selection of a machine-learning algorithm from the plurality of machine-learning algorithms through the graphical user interface; and

in response to the selection of the selected machine-learning algorithm from the plurality of machine-learning algorithms:

iteratively executing the selected machine-learning algorithm, by using a parameter of the selected machine-learning algorithm as an iteration variable to process the set of values of the particular data set and generate a plurality of machine-learning models;

determining one or more comparison metric values for data output by each machine-learning model of the plurality of machine-learning models; and

causing presentation of the one or more comparison metric values for the data output by the plurality of machine-learning models,

wherein at least a part of the method is performed by one or more processors.

2 . The method of claim 1 , further comprising determining a plurality of first parameters within the selected machine-learning algorithm as a plurality of first iteration variables.

3 . The method of claim 2 , wherein the selected machine-learning algorithm is a first machine-learning algorithm, wherein the method further comprises determining a plurality of second parameters within a second machine-learning algorithm of the plurality of machine-learning algorithms as a plurality of second iteration variables, wherein the second machine-learning algorithm is different from the first machine-learning algorithm.

4 . The method of claim 3 , further comprising determining a first iteration order for the plurality of first iteration variables.

5 . The method of claim 4 , further comprising determining a second iteration order for the plurality of second iteration variables.

6 . The method of claim 2 , wherein iteratively executing the selected 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 plurality of first parameters within the selected machine-learning algorithm.

7 . The method of claim 3 , further comprising:

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 plurality of second parameters within the second machine-learning algorithm.

8 . The method of claim 1 , wherein the presentation of the one or more 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 of the plurality of machine-learning models, the result comprising at least one of the one or more comparison metric values.

9 . 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 one or more selectable graphical interface elements.

10 . A computer implemented system, comprising:

one or more memories having instructions stored thereon; and

one or more processors configured to execute the instructions and perform operations comprising:

causing presentation of a graphical user interface having one or more selectable graphical interface elements representing a set of data sets and a set of model families, the set of data sets including a particular data set including a set of values, and the set of model families including a plurality of machine-learning algorithms;

receiving a selection of a machine-learning algorithm from the plurality of machine-learning algorithms through the graphical user interface; and

in response to the selection of the selected machine-learning algorithm:

iteratively executing the selected machine-learning algorithm, by using a parameter of the selected machine-learning algorithm as an iteration variable to process the set of values of the particular data set and generate a plurality of machine-learning models;

determining one or more comparison metric values for data output by each machine-learning model of the plurality of machine-learning models; and

causing presentation of the one or more comparison metric values for the data output by the plurality of machine-learning models.

11 . The system of claim 10 , wherein the operations further comprise determining a plurality of first parameters within the selected machine-learning algorithm as a plurality of first iteration variables.

12 . The system of claim 11 , wherein the selected machine-learning algorithm is a first machine-learning algorithm, wherein the operations further comprise determining a plurality of second parameters within a second machine-learning algorithm of the plurality of machine-learning algorithms as a plurality of second iteration variables, and wherein the second machine-learning algorithm is different from the first machine-learning algorithm.

13 . The system of claim 12 , wherein the operations further comprise determining a first iteration order for the plurality of first iteration variables.

14 . The system of claim 13 , wherein the operations further comprise determining a second iteration order for the plurality of second iteration variables.

15 . The system of claim 11 , wherein iteratively executing the selected 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 plurality of first parameters within the selected machine-learning algorithm.

16 . The system of claim 12 , wherein the operations further comprise

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 plurality of second parameters within the second machine-learning algorithm.

17 . The system of claim 10 , wherein the presentation of the one or more 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 one or more comparison metric values.

18 . The system of claim 10 , 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 one or more selectable graphical interface elements.

19 . 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 presentation of a graphical user interface having one or more selectable graphical interface elements representing a set of data sets and a set of model families, the set of data sets including a particular data set including a set of values, and the set of model families including a plurality of machine-learning algorithms;

receiving a selection of a machine-learning algorithm from the plurality of machine-learning algorithms through the graphical user interface; and

in response to selection of the selected machine-learning algorithm:

iteratively executing the selected machine-learning algorithm, by using a parameter of the selected machine-learning algorithm as an iteration variable to process the set of values of the particular data set and generate a plurality of machine-learning models;

determining one or more comparison metric values for data output by each machine-learning model of the plurality of machine-learning models; and

causing presentation of the one or more comparison metric values for the data output by the plurality of machine-learning models.

20 . The non-transitory machine-readable storage device of claim 19 , wherein the presentation of the one or more 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 one or more comparison metric values.