Model-agnostic interpretation by visualization of feature perturbations

01/26/2021
by   Wilson E. Marcílio-Jr, et al.
1

Interpretation of machine learning models has become one of the most important topics of research due to the necessity of maintaining control and avoid bias in these algorithms. Since many machine learning algorithms are published every day, there is a need for novel model-agnostic interpretation approaches that could be used to interpret a great variety of algorithms. One particularly useful way to interpret machine learning models is to feed different input data to understand the changes in the prediction. Using such an approach, practitioners can define relations among patterns of data and a model's decision. In this work, we propose a model-agnostic interpretation approach that uses visualization of feature perturbations induced by the particle swarm optimization algorithm. We validate our approach both qualitatively and quantitatively on publicly available datasets, showing the capability to enhance the interpretation of different classifiers while yielding very stable results if compared with the state of the art algorithms.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/11/2018

Global Model Interpretation via Recursive Partitioning

In this work, we propose a simple but effective method to interpret blac...
research
04/08/2019

Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations

Non-linear machine learning models often trade off a great predictive pe...
research
08/09/2021

Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models

Variable importance, interaction measures, and partial dependence plots ...
research
05/14/2021

Information-theoretic Evolution of Model Agnostic Global Explanations

Explaining the behavior of black box machine learning models through hum...
research
04/09/2021

Transforming Feature Space to Interpret Machine Learning Models

Model-agnostic tools for interpreting machine-learning models struggle t...
research
08/01/2018

Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models

Interpretation and diagnosis of machine learning models have gained rene...

Please sign up or login with your details

Forgot password? Click here to reset