In this paper, we focus on improving the single-tree method and propose the segmented linear regression trees (SLRT) model that replaces the traditional constant leaf model with linear ones. From the parametric view, SLRT can be employed as a recursive change point detect procedure for ...
Both random forests and linear kernel support vector machines (SVM) were tested in addition to logistic regression. The posterior probability from the random forests yielded similar performance to logistic regression, but at the cost of increased runtime. The SVM proved too slow to use for the 71...
An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests Psychol. Methods, 14 (4) (2009), pp. 323-348 https://doi.org/10.1037/a0016973 CrossrefView in ScopusGoogle Scholar Touw et al., 2013 W.G. ...
XGBoost [44], as a tree integration model, sums the results of K trees, where is the final predicted value: (1) Here, K represents the number of trees, fk is the model of the k tree, and ε is the learning rate. Supposing that there are n samples and m features in a given sa...
This paper proposes a partitioning structure learning method for segmented linear regression trees (SLRT), which assigns linear predictors over the terminal nodes. The recursive partitioning process is driven by an adaptive split selection algorithm that maximizes, at each node, a criterion function ...
(e.g. boosted trees or support vector machines) and models such as Naive Bayes produce distorted outputs, which require a calibration step to align the ranking with the true class posterior probability [37,38]. Conversely, the logistic regression output aligns with the true class posterior ...
Parameter estimates and statistics of Equation (1) fitted by a combined method of constrained two-dimensional optimum seeking and least square regression (CTOS & LSR) and the unconstrained least square regression (ULSR) for nine individual trees (three trees from each species of C. hystrix, E....