Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions
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Updated
Nov 22, 2018 - C++
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions
Describe data in terms of informative and concise sets of patterns
counterfactual explanations for XGBoost and tree ensemble models - counterfactual reasoning - model interpretability
Re-implementation of GP-GOMEA that attempts to be simpler to understand and use than the original.
Fit interpretable models. Explain blackbox machine learning.
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