How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level
Table 4: Average accuracy, log-loss, AUROC for bills using legislative events post introduction.
['Feature Set', 'Accuracy Average', 'Accuracy SD', 'Log-Loss Average', 'Log-Loss SD', 'AUROC Average', 'AUROC SD']
Accuracy improves to 0.94, while LL drops by half to 0.16, confirming that legislative events occurring up to the point point of floor action carry significant complementary information to other contextual factors and are highly indicative of floor action. While combined+act confirms the predictive power of procedural factors outside the legislative text, sponsor, and committee assignment, the combined model is arguably the most important result, as it indicates how well we can predict on features that are available upon introduction.