The potential benefits of data set filtering and learning algorithm hyperparameter optimization
Michael R. Smith, Tony R. Martinez, Christophe G. Giraud-Carrier · 2015
The quality of a model induced by a learning algorithm is dependent upon the training data and the hyperparameters supplied to the learning algorithm. Prior work has shown that a model's quality can be significantly improved by filtering out low quality instances or by tuning the learning algorithm hyperparameters. The potential impact of filtering and hyperparameter optimization (HPO) is largely unknown. In this paper, we estimate the potential benefits of instance filtering and HPO. While both HPO and filtering significantly improve the quality of the induced model, we find that filtering has a greater potential effect on the quality of the induced model than HPO, motivating future work in filtering.