SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 381–390 of 537 papers

TitleStatusHype
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations—0
An exact counterfactual-example-based approach to tree-ensemble models interpretabilityCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Analysis and classification of main risk factors causing stroke in Shanxi Province—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots—0
Comparing interpretability and explainability for feature selection—0
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning—0
Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled ExperimentsCode0
Show:102550
← PrevPage 39 of 54Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Q-SENNTop 1 Accuracy85.9—Unverified
2SLDD-ModelTop 1 Accuracy85.7—Unverified