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| courses:xai:p1_2024 [2024/11/22 11:28] – [PPCF Counterfactual explainer evaluation] admin | courses:xai:p1_2024 [2024/11/22 12:35] (current) – [Explainable Actrive Learning] admin | ||
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| The goal is to run hyperparameter optimization algorithm such as optuna, collect results from it and use SHAP to try to explain the impact of different hyperparameters on the quality of the optimization process | The goal is to run hyperparameter optimization algorithm such as optuna, collect results from it and use SHAP to try to explain the impact of different hyperparameters on the quality of the optimization process | ||
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| + | ===== Explainable Active Learning | ||
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| + | The goal is to use SHAP gradients (implemented in LUX) to select instances that should be labelled by expert (Active Learning paradigm) and compare with random sampling. | ||
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