Interpretable models for extrapolation in scientific machine learning

Making machine learning models interpretable

Data of different levels of complexity and of ever growing diversity of characteristics are the raw materials that machine learning practitioners try to model using their wide palette of methods and tools. The obtained models are meant to be a synthetic representation of the available, observed data that captures some of their intrinsic regularities or patterns. Therefore, the use of machine le...

Research directions in interpretable machine learning models

The theoretical novelty of many machine learning methods leading to high performing algorithms has been substantial. However, the black-box nature of much of this body of work has meant that the models are difficult to interpret, with the consequence that the significant developments in machine learning theory are not matched by their practical impact. This tutorial stresses the need for interp...

Interpretable Machine Learning Models for the Digital Clock Drawing Test

The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions. The Digital Clock Drawing Test (dCDT) uses novel software to analyze data from a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making possible the analysis of both the drawing process and final product. We developed ...

A NOTE TO INTERPRETABLE FUZZY MODELS AND THEIR LEARNING

In this paper we turn the attention to a well developed theory of fuzzy/lin-guis-tic models that are interpretable and, moreover, can be learned from the data.We present four different situations demonstrating both interpretability as well as learning abilities of these models.

Learning interpretable models