methodology-19
Interpretable Machine Learning
3260 papers • 126 benchmarks • 313 datasets
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
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CUB-200-2011
CUB-200-2011
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