Interpretable Machine Learning

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 141–150 of 537 papers

Title	Date	Tasks	Status	Score
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence	Apr 27, 2021	BIG-bench Machine LearningInterpretable Machine Learning	CodeCode Available	5
Hyperspectral Blind Unmixing using a Double Deep Image Prior	Aug 21, 2023	Hyperspectral UnmixingImage Generation	CodeCode Available	5
iNNvestigate neural networks!	Aug 13, 2018	Interpretable Machine Learning	CodeCode Available	5
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data	Jan 3, 2025	Additive modelsInterpretable Machine Learning	CodeCode Available	5
How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning	Nov 10, 2021	Band GapBIG-bench Machine Learning	CodeCode Available	5
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine	May 30, 2021	image-classificationImage Classification	CodeCode Available	5
Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute	Feb 12, 2018	Decision MakingInterpretable Machine Learning	CodeCode Available	5
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets	Jun 4, 2022	Interpretable Machine Learning	CodeCode Available	5
Harnessing Interpretable Machine Learning for Holistic Inverse Design of Origami	Apr 12, 2022	BIG-bench Machine LearningInterpretable Machine Learning	CodeCode Available	5
Branches: Efficiently Seeking Optimal Sparse Decision Trees with AO*	Jun 4, 2024	Computational EfficiencyInterpretable Machine Learning	CodeCode Available	5

Show:10 25 50

← PrevPage 15 of 54Next →

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Q-SENN	Top 1 Accuracy	85.9	—	Unverified
2	SLDD-Model	Top 1 Accuracy	85.7	—	Unverified