SnapToGrid: From Statistical to Interpretable Models for Biomedical Information Extraction
Table 3: Performance of the statistical model with L1-regularized LR, using only features that can be converted to rules.
['Event Class', 'Recall', 'Precision', 'F1']
Importantly, a system with more than 1 million features is not interpretable. To address this, we trained the same system using L1 regularization as a form of feature selection. This reduced the number of features with non-zero weights by two orders of magnitude: from over 1 million to 10,926. The results demonstrate that this drastic reduction in the number of useful features came with a small performance cost, of less than 1 F1 point.