Automatically balancing accuracy and comprehensibility in predictive modeling
Ulf Johansson, Rikard König, Lars Niklasson · 2005
One specific problem, when performing predictive modeling, is the tradeoff between accuracy and comprehensibility. When comprehensible models are required, this normally rules out high-accuracy techniques like neural networks and committee machines. Therefore, an automated choice of a standard technique, known to generally produce sufficiently accurate and comprehensible models, would be of great value. In this paper, it is argued that this requirement is met by an ensemble of classifiers, followed by rule extraction. The proposed technique is demonstrated, using an ensemble of common classifiers and our rule extraction algorithm G-REX, on 17 publicly available data sets. The results presented demonstrate that the suggested technique performs very well. More specifically, the ensemble clearly outperforms the individual classifiers regarding accuracy, while the extracted models have accuracy similar to the individual classifiers. The extracted models are, however, significantly more compact than corresponding models created directly from the data set using the standard tool CART; thus providing higher comprehensibility.