Abstract
We introduce Natural Learning (NL), a novel algorithm that elevates the explainability and interpretability of machine learning to an extreme level. NL simplifies decisions into intuitive rules, like "We rejected your loan because your income, employment status, and age collectively resemble a rejected prototype more than an accepted prototype." When applied to real-life datasets, NL produces impressive results. For example, in a colon cancer dataset with 1545 patients and 10935 genes, NL achieves 98.1% accuracy, comparable to DNNs and RF, by analyzing just 3 genes of test samples against 2 discovered prototypes. Similarly, in the UCI's WDBC dataset, NL achieves 98.3% accuracy using only 7 features and 2 prototypes. Even on the MNIST dataset (0 vs. 1), NL achieves 99.5% accuracy with only 3 pixels from 2 prototype images. NL is inspired by prototype theory, an old concept in cognitive psychology suggesting that people learn single sparse prototypes to categorize objects. Leveraging thi