How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level
Table 3: Average and standard deviation across states on accuracy, log-loss, AUROC for bills on each feature set.
['Feature Set', 'Accuracy Average', 'Accuracy Std Dev', 'Log-Loss Average', 'Log-Loss Std Dev', 'AUROC Average', 'AUROC Std Dev']
The just_txt model achieves an accuracy of 73%, outperforming the baseline by 5%, and notably, shows that there is a predictive signal even within the limited amount of text available in the title and descriptions.