Data processing and error detection and correction for artificial intelligence systems

A non-transitory computer-readable medium comprising executable instructions for training and optimizing machine learning models, wherein the executable instructions include:

• loading a training data set, wherein the training data set includes a first bin and a second bin;
• applying an under-sampling technique to elements of the first bin to generate an updated first bin;
• applying an over-sampling technique to elements of the second bin to generate an updated second bin;
• generating an updated training data set by merging the updated first bin and the updated second bin;
• loading baseline hyperparameters;
• configuring a machine learning model with the baseline hyperparameters;
• providing the updated training data set as inputs to the machine learning model configured with the baseline hyperparameters to determine baseline performance metrics;
• determining whether the baseline performance metrics are above a threshold;
• in response to determining that the baseline performance metrics are above the threshold, saving the baseline hyperparameters as optimal hyperparameters;
• configuring the machine learning model with optimal hyperparameters;
• and
• providing input variables to the machine learning model configured with the optimal hyperparameters to generate output variables.