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 31–40 of 537 papers

TitleStatusHype
TSInterpret: A unified framework for time series interpretabilityCode1
ControlBurn: Nonlinear Feature Selection with Sparse Tree EnsemblesCode1
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm ExecutionCode1
Neural Basis Models for InterpretabilityCode1
Scalable Interpretability via PolynomialsCode1
Towards Better Understanding Attribution MethodsCode1
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector MachinesCode1
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
POTATO: exPlainable infOrmation exTrAcTion framewOrkCode1
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Benchmark Results

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