| CPC G06N 3/0464 (2023.01) | 20 Claims |
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1. An apparatus for machine learning model generation, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
obtain system state data, wherein the system state data comprises a plurality of points;
receive a metamodel training data set, wherein the metamodel training data set comprises historical system state data and historical machine learning model forms which were successfully applied to the historical system state data;
sanitize the metamodel training data set using a dedicated hardware unit comprising circuitry configured to perform signal processing operations, wherein sanitizing the metamodel training data set comprises:
determining by the dedicated hardware unit that at least one training data entry of the metamodel training data set has a signal to noise ratio below a threshold value; and
removing the at least one training data entry from the metamodel training data set to create sanitized metamodel training data set;
iteratively train a metamodel as a function of the sanitized metamodel training data set;
generate a machine learning model form as a function of the trained metamodel, wherein generating the machine learning model form comprises:
determining a visual element data structure as a function of the generated machine learning model form, wherein determining the visual element data structure comprises:
ranking the plurality of points of the system state data;
applying rules based on a comparison between a ranking; and
transmitting the visual element data structure to a display device;
detect a divergence in the system state data as a function of feedback related to an effectiveness of the machine learning model form; and
retrain the metamodel using one or more results indicating the effectiveness of the machine learning model form.
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