Continuous target variable prediction with augmented interval pattern structures: Lazy algorithm
Pattern structures are known to provide a tool for predictive modeling and classification. However, in order to generate classification rules concept lattice should be built. This procedure may take much time and resources. In previous work it was shown that it is possible to escape the problem with so-called lazy associative classification algorithm. It does not require lattice construction and it is applicable to classification problems such as credit scoring. In this paper we adjust this method to the case of continuous target variable, i.e. regression problem, and apply it to recovery rates forecasting. We perform parameters tuning, assess the accuracy of the algorithm based on the bank data and compare it to the models adopted in the bank system and other benchmarks.