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 91–100 of 537 papers

TitleStatusHype
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics—0
An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia—0
midr: Learning from Black-Box Models by Maximum Interpretation DecompositionCode0
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data—0
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100—0
Data Model Design for Explainable Machine Learning-based Electricity Applications—0
Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study—0
Are machine learning interpretations reliable? A stability study on global interpretations—0
Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients—0
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy—0
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Benchmark Results

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