BACKGROUND

Field

The present disclosure generally relates to a model training engine for creating and/or modifying a machine learning model. More specifically, the present disclosure relates to a graphical user interface for creating, updating, training, and testing a model with the model training engine.

Description of the Related Art

Machine learning algorithms are being used in a widening range of fields. As a result, the coding of these complex algorithms has become a niche application for savvy computer scientists or experienced programmers well versed in various source code languages. While this trend leaves aside a number of people who may know best how to apply the algorithm to solve a specific problem, it also hinders the work of even the most savvy of experts when attempting to work with inconsistent databases or programming templates. Accordingly, it is desirable to have a common platform for accessing a model training engine by non-experts and experts alike, who can access, use, test, and create machine learning models, new and already existing. Programming experts would benefit from such platform for its ability to incorporate already existing models in a variety of source code resources, the simple visualization of results, debugging, and the ability to combine multiple machine learning schemes to solve a specific problem.

SUMMARY

In one embodiment of the present disclosure, a computer-implemented method is described for creating one or more machine learning models in a model training engine. The computer-implemented method includes providing to a user, via a graphical user interface, a selection of components for a machine learning model, at least one component having a computational layer including one or more coefficients associated with a component attribute. The computer-implemented method also includes displaying, in the graphical user interface, a component selected by the user, including a selected value of the component attribute and executing the machine learning model with a training archive as an input, to obtain an output indicative of a desired classification of the training archive. The computer-implemented method also includes comparing the output with a desirable feature value, and modifying at least one coefficient in the component of the machine learning model based on a difference between the output from the machine learning model and the desirable feature value.

According to one embodiment, a system is described that includes one or more processors and a memory coupled to the one or more processors, the memory including instructions that, when executed by the one or more processors, cause the one or more processors to provide to a user, via a graphical user interface, a selection of components for a machine learning model, at least one component having a computational layer including one or more coefficients associated with a component attribute. The one or more processors also execute instructions to display, in the graphical user interface, a component selected by the user, including a selected value of the component attribute, to form an executable model based on the machine learning model including the component selected by the user and the selected value of the component attribute, and to execute the machine learning model with a training archive as an input, to obtain an output. The one or more processors also execute instructions to compare the output with a desirable feature value and to modify at least one coefficient in the component of the machine learning model based on a difference between the output from the machine learning model and the desirable feature value.

According to one embodiment, a non-transitory, machine-readable medium is described that includes instructions, which when executed by one or more processors, cause a computer to perform a method, including providing to a user, via a graphical user interface, a selection of components for a machine learning model, at least one component having a computational layer including one or more coefficients associated with a component attribute. The method also includes displaying, in the graphical user interface, a component selected by the user, including a selected value of the component attribute, forming an executable model based on the machine learning model including the component selected by the user and the selected value of the component attribute, and executing the machine learning model with a training archive as an input, to obtain an output. The method also includes comparing the output with a desirable feature value, modifying at least one coefficient in the component of the machine learning model based on a difference between the output from the machine learning model and the desirable feature value, and alerting the user, via a graphic indicator, of a compiling error in the machine learning model, including a graphical explanation of an error source.

In yet another embodiment, a system is described that includes a means for storing commands and a means for executing the commands causing the system to perform a method that includes providing to a user, via a graphical user interface, a selection of components for a machine learning model, at least one component having a computational layer including one or more coefficients associated with a component attribute. The method also includes displaying, in the graphical user interface, a component selected by the user, including a selected value of the component attribute, forming an executable model based on the machine learning model including the component selected by the user and the selected value of the component attribute, and executing the machine learning model with a training archive as an input, to obtain an output. The method also includes comparing the output with a desirable feature value, modifying at least one coefficient in the component of the machine learning model based on a difference between the output from the machine learning model and the desirable feature value, and alerting the user, via a graphic indicator, of a compiling error in the machine learning model, including a graphical explanation of an error source.

It is understood that other configurations of the subject technology will become readily apparent to those skilled in the art from the following detailed description, wherein various configurations of the subject technology are shown and described by way of illustration. As will be realized, the subject technology is capable of other and different configurations and its several details are capable of modification in various other respects, all without departing from the scope of the subject technology. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive.

