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

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Showing 351–360 of 537 papers

TitleStatusHype
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots—0
Comparing interpretability and explainability for feature selection—0
Interpretable machine learning for high-dimensional trajectories of aging healthCode1
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning—0
Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled ExperimentsCode0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Causality-based Counterfactual Explanation for Classification ModelsCode0
Online Product Feature Recommendations with Interpretable Machine Learning—0
Do Feature Attribution Methods Correctly Attribute Features?Code1
From Human Explanation to Model Interpretability: A Framework Based on Weight of EvidenceCode0
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Benchmark Results

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