Predicting and Analyzing Law-Making in Kenya
Table 1: Evaluation metrics with results (average for both classes). For Brier score, lower is better.
['[BOLD] Model', '[BOLD] F1', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] AUC', '[BOLD] Brier Loss', '[BOLD] Accuracy']
With a 70:30 train-test split on our data, we employed Logistic Regression and Support Vector Machine models to obtain baseline results before proceeding to stack these models as base learners and then used another Logistic Regression classifier as the meta-learner in a bid to obtain better results. While handling the data imbalance problem improved the base models with a 5% and 11% increase in the precision of both classes, the results obtained for the enacted class after oversampling remained the same. However, we obtained impressive results for predicting that a bill will not be enacted. For better context, this means when a new bill is proposed and fed to the final model with all relevant aforementioned features, the model is 81% accurate in predicting if the bill will be passed into law or not. The precision and recall are 65% and 71% respectively.