Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over th...

In this paper, we model an optimal regression tree through a continuous optimization problem, where compromise between prediction accuracy and both types of sparsity, namely local global, is sought. Our approach can accommodate important desirable properties for the task, such as cost-sensitivity fairness. Thanks to smoothness predictions, derive explanations on predictor variables. The computa...

Nowadays decision tree learning is one of the most popular classification and regression techniques. Though decision trees are not accurate on their own, they make very good base learners for advanced tree-based methods such as random forests and gradient boosted trees. However, applying ensembles of trees deteriorates interpretability of the final model. Another problem is that decision tree l...

Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is tackled as a classification task while the latt...

Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is tackled as a classification task while the latt...

................................................................................................................................................. 3 INTRODUCTION ........................................................................................................................................ 4 LITERATURE REVIEW ..................................................................................

This paper briefly surveys existing methods for boosting multi-class classication algorithms, as well as compares the performance of one such implementation, Stagewise Additive Modeling using a Multi-class Exponential loss function (SAMME), against that of Softmax Regression, Classification and Regression Trees, and Neural Networks.