An Evaluation System for Interpretable Machine Learning

A book on the research of the Evaluation Systems for Interpretable Models

An Evaluation System

Authors:

Prabhu Subramanian - LinkedIn | GitHub

(MS in Information Systems student at Northeastern University College of Engineering, Boston)

Mentor:

Nicholas Brown - LinkedIn | GitHub

(Assistant Teaching Professor, Multidisciplinary Graduate Engineering Programs, Northeastern University)

The Evaluation System of Interpretable Machine Learning

1. Abstract

2. Introduction

3. Interpretable Machine Learning

3.1 Definition

3.2 Method

• 3.2.1 Machine Learning Model

• 3.2.2 Model-Agnostic

• 3.2.3 Sample Theory

4. Evaluation System of Interpretable Machine Learning

4.1 Definition

4.2 The Structure of the Evaluation System

• 4.2.1 Interpretable models(Model-Specific)

• 4.2.2 Model-Agnostic

• 4.2.3 Human Explanations

4.3 Reference Score Table

5. Customized - Interpretable Machine Learning Model

5.1 Why & How the user customize their model?

5.2 Example

• 5.2.1 Suggestion Lists

• 5.2.2 Notebook

5.3 Results & Explanation

6. Discussion

7. Citation and License