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While machine learning and deep learning models often produce good classifications and predictions, they are almost never perfect. Models almost always have some percentage of false positive and false negative predictions. That’s sometimes acceptable, but matters a lot when the stakes are high. For example, a drone weapons system that falsely identifies a school as […]

Someday machine learning models may be more ‘glass box‘ than black box. Until then, explainability tools and techniques can help us understand how a black box model makes its decisions.