Flexible and Optimal M5 Model Trees with Applications to Flow Predictions

M5 is a method developed by Quinlan [10] for inducing trees of linear regression models (model trees). The paper addresses the flexibility and optimality in M5 model tree by proposing two new algorithms, namely M5flex and M5opt. M5flex algorithm brings in domain knowledge by enabling the user to choose split attributes and split values for important nodes in a model tree so that the resulting model would be more accurate, reliable and appropriate for practical applications. M5opt is a semi-non-greedy algorithm with a number of improvements if compared with M5. For experiments six hydrological data sets and five benchmark data sets were used. For comparison, M5’ and ANN algorithms were employed as well. Overall, M5flex was the most accurate, followed by M5opt, M5’ and ANN.