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 331–340 of 537 papers

TitleStatusHype
Towards Probabilistic Dynamic Security Assessment and Enhancement of Large Power Systems—0
Towards Simple Machine Learning Baselines for GNSS RFI Detection—0
Tribe or Not? Critical Inspection of Group Differences Using TribalGram—0
Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning—0
Unfolding Tensors to Identify the Graph in Discrete Latent Bipartite Graphical Models—0
Using an interpretable Machine Learning approach to study the drivers of International Migration—0
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data—0
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes—0
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models—0
Variable Selection via Thompson Sampling—0
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Benchmark Results

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