General intelligence for tabular data

A system for generating a contextually adaptable machine learning (ML) based classifier model, the system comprising:

• one or more processors;
• and
• a memory communicatively coupled with the one or more processors and storing instructions that, when executed by the one or more processors, causes the system to perform operations including:
• obtaining a dataset including datapoints, feature values characterizing the datapoints according to input features, and labels classifying the datapoints according to a target label;
• transforming each respective datapoint into a natural language statement (NLS), each NLS associating the respective datapoint's feature values with feature identifiers assigned to the corresponding input features, and each NLS associating the respective datapoint's label with a label identifier assigned to the target label;
• generating a feature matrix for each NLS based on the feature identifiers and feature values;
• transforming the feature matrix into a global feature vector;
• generating a target vector for each NLS based on the label identifier and the corresponding label;
• transforming the target vector into a global target vector having a same shape as the global feature vector;
• and
• generating, using the global feature vector and the global target vector in conjunction with a similarity measurement operation and a loss function, an ML-based classifier model trained to generate, using one or more neural network models, a compatibility score predictive of an accuracy at which the classifier model can classify given data based on at least one of a different feature characterizing the given data or a different label for classifying the given data.