Synthesizing a singular ensemble machine learning model from an ensemble of models

A system for generating a singular ensemble model, comprising:

• at least one processor;
• and
• at least one storage medium storing instructions that, when executed, configure the processor to perform operations comprising:
• obtaining a plurality of machine learning models;
• obtaining a training data set;
• applying the plurality of machine learning models to at least a portion of the training data set to obtain outputs associated with the models;
• mapping each output of a respective model to features of the respective model;
• combining the mapped features into a singular machine learning model;
• training the singular machine learning model using at least another portion of the training data set;
• and
• outputting the trained singular machine learning model, the outputting comprising transmitting the trained singular machine learning model to a client device, the client device being configured to:
• store a data structure defining parameters of the trained singular machine learning model;
• retrieve input data for the trained singular machine learning model;
• apply the trained singular machine learning model to the input data to generate results;
• and
• display the results.

A method for generating a singular ensemble model, comprising:

• obtaining a plurality of machine learning models;
• obtaining a plurality of training data sets, each set corresponding to one of the models;
• applying the plurality of machine learning models to at least a portion of the corresponding training data sets to obtain output sets associated with the models, each output set corresponding to one of the models,
• combining the output sets to form a final output set,
• mapping each final output set of a respective model to features of the respective model;
• combining the mapped features of the models into a singular machine learning model;
• training the singular machine learning model using at least another portion of the corresponding training data sets;
• and
• outputting the trained singular machine learning model, the outputting comprising transmitting the trained singular machine learning model to a client device, the client device being configured to:
• store a data structure defining parameters of the trained singular machine learning model;
• retrieve input data for the trained singular machine learning model;
• apply the trained singular machine learning model to the input data to generate results;
• and
• display the results.

A method for generating a singular ensemble model, comprising:

• obtaining a plurality of machine learning models;
• obtaining, from an input recognizer, a training data set, the training data set being consistent with the plurality of machine learning models;
• applying the plurality of machine learning models to at least a portion of the training data set to obtain outputs associated with the models;
• mapping each output of a respective model to features of the respective models;
• combining the mapped features of the models into a singular machine learning model;
• applying the singular machine learning model to the training data set to obtain output;
• comparing the outputs to the output of the singular machine learning model;
• adjusting one or more parameters of the singular machine learning model based on the comparison;
• determining whether the comparing and the adjusting comply with at least one threshold, wherein the threshold comprises at least one of a direct threshold or a threshold relative to a last iteration of parameter adjustment;
• and
• outputting the trained singular machine learning model to a client device, the client device being configured to:
• store a data structure defining parameters of the trained singular machine learning model;
• retrieve input data for the trained singular machine learning model;
• apply the trained singular machine learning model to the input data to generate results;
• and
• display the results.