Log In Sign Up

An Active Approach for Model Interpretation

by   Jialin Lu, et al.

Model interpretation, or explanation of a machine learning classifier, aims to extract generalizable knowledge from a trained classifier into a human-understandable format, for various purposes such as model assessment, debugging and trust. From a computaional viewpoint, it is formulated as approximating the target classifier using a simpler interpretable model, such as rule models like a decision set/list/tree. Often, this approximation is handled as standard supervised learning and the only difference is that the labels are provided by the target classifier instead of ground truth. This paradigm is particularly popular because there exists a variety of well-studied supervised algorithms for learning an interpretable classifier. However, we argue that this paradigm is suboptimal for it does not utilize the unique property of the model interpretation problem, that is, the ability to generate synthetic instances and query the target classifier for their labels. We call this the active-query property, suggesting that we should consider model interpretation from an active learning perspective. Following this insight, we argue that the active-query property should be employed when designing a model interpretation algorithm, and that the generation of synthetic instances should be integrated seamlessly with the algorithm that learns the model interpretation. In this paper, we demonstrate that by doing so, it is possible to achieve more faithful interpretation with simpler model complexity. As a technical contribution, we present an active algorithm Active Decision Set Induction (ADS) to learn a decision set, a set of if-else rules, for model interpretation. ADS performs a local search over the space of all decision sets. In every iteration, ADS computes confidence intervals for the value of the objective function of all local actions and utilizes active-query to determine the best one.


page 1

page 2

page 3

page 4


Disagreement-based Active Learning in Online Settings

We study online active learning for classifying streaming instances with...

Online Active Learning: Label Complexity vs. Classification Errors

We study online active learning for classifying streaming instances. At ...

Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation

We propose a new active learning (AL) framework, Active Learning++, whic...

Active Learning from Imperfect Labelers

We study active learning where the labeler can not only return incorrect...

Geometry of Friston's active inference

We reconstruct Karl Friston's active inference and give a geometrical in...

CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output

It is often infeasible or impossible to obtain ground truth labels for m...

Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler

Active learning with strong and weak labelers considers a practical sett...