Topic: xai-evaluation Goto Github
Some thing interesting about xai-evaluation
Some thing interesting about xai-evaluation
xai-evaluation,CNN architectures Resnet-50 and InceptionV3 have been used to detect whether the CT scan images is covid affected or not and prediction is validated using explainable AI frameworks LIME and GradCAM.
User: akashvs01
xai-evaluation,Explainable and trustworthy AI Course
Organization: dbdmg
xai-evaluation,
User: enantiodromis
xai-evaluation,Replication package for the KNOSYS paper titled "An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability".
User: francesco-sovrano
Home Page: https://doi.org/10.5281/zenodo.10478812
xai-evaluation,Classify applications using flow features with Random Forest and K-Nearest Neighbor classifiers. Explore augmentation techniques like oversampling, SMOTE, BorderlineSMOTE, and ADASYN for better handling of underrepresented classes. Measure classifier effectiveness for different sampling techniques using accuracy, precision, recall, and F1-score.
User: ggjay9
xai-evaluation,ConsisXAI is an implementation of a technique to evaluate global machine learning explainability (XAI) methods based on feature subset consistency
User: ghadaelkhawaga
xai-evaluation,Semantic Meaningfulness: Evaluating counterfactual approaches for real world plausibility
User: jhoelli
xai-evaluation,Open and extensible benchmark for XAI methods
User: oxid15
Home Page: https://oxid15.github.io/xai-benchmark/
xai-evaluation,Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems
User: sinamohseni
xai-evaluation,Research on AutoML and Explainability.
User: yuutoaiza
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