Anchor your predictions

The Anchors algorithm, proposed by Ribeiro, Singh, and Guestrin in 2018, offers a method for conducting error analysis in machine learning by providing local explanations of model predictions through decision rules. Unlike LIME, which uses a linear model for approximation, Anchors defines a coverage interval for a decision rule's applicability with a certain probability, emphasizing a trade-off between precision and coverage. This method has been applied to classify breast tumors in the Wisconsin dataset, revealing the model's reliance on features like area_worst and concavity to differentiate between malign and benign tumors. By setting a precision threshold, users can explore model reasoning, gaining insights into feature importance and decision boundaries, although more rules can lead to higher precision but reduced coverage. The process aids in understanding predictions locally and encourages experimentation with different thresholds to enhance model interpretability.