The main aim of this paper is to develop a suitable regression analysis model for describing the relationship between the index efficiency and the parameters of the Rival Penalized Competitive Learning Binary Tree (RPCLb-tree). RPCL-b-tree is a hierarchical indexing structure built with a hierarchical RPCL clustering implementation, which transforms the feature space into a sequence of nested c...

Abstract In binary and ordinal regression one can distinguish between a location component scaling component. While the former determines within range of response categories, indicates variance heterogeneity. particular since it has been demonstrated that misleading effects occur if ignores presence component, is important to account for potential in model, which not possible available recursiv...

Drafting strong players is crucial for a team’s success. We describe a new data-driven interpretable approach for assessing draft prospects in the National Hockey League. Successful previous approaches have 1) built a predictive model based on player features (e.g. Schuckers 2017 [ESSC16], or 2) derived performance predictions from the observed performance of comparable players in a cohort (Wei...

We evaluated effects of atmospheric deposition of nitrogen on the composition of forest understorey vegetation both in space and time, using repeated data from the European wide monitoring program ICP-Forests, which focuses on normally managed forest. Our aim was to assess whether both spatial and temporal effects of deposition can be detected by a multiple regression approach using data from m...

Recently ordinal regression has attracted much interest in machine learning. The goal of ordinal regression is to assign each instance a rank, which should be as close as possible to its true rank. We propose an effective tree-based algorithm, called Ranking Tree, for ordinal regression. The main advantage of Ranking Tree is that it can group samples with closer ranks together in the process of...