Logistic Retrogression Model for Evaluating the Influence of Environmental Factors on Legislative Data

Applicability of artificial intelligence techniques, in evaluating the influence of the environmental factors in legislative data was found amenable in an earlier study - SVM performed to satisfying results with a 21.5 percent error rate for passage of legislation as compared to the performance of ANN at 28 percent error rate and K-NN at 29 percent error rate. These techniques reported both collective influence (ANN, K-NN and SVM) and respective influence (SVM one-against-all classifier). Determining the environmental influences - individually or in combination with other factors, could only be measurably achieved using other modeling techniques, despite SVM with probabilistic output of 76 percent outperforming PNN with 71 percent out. A triangulation of both statistical and artificial intelligence modeling techniques in classification is thus proposed for decision making support in legislative drafting, given that computations involving statistical approach correctly predicted up to 98.20 percent and placed economic considerations as the most important factor for the passing of a bill with economic connotations. Other predictions involving political, social, cultural factors did not however, perform as well as the PNN and SVM with probabilistic output.