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Decision trees are a popular statistical learning algorithm for classification and regression that recursively split the data based on the most informative characteristics. Unfortunately, they do not have a high predictive power with respect to other statistical learning methods. To enhances their performances, this paper proposes a support vector machine approach to create oblique decision trees for regression problems. In this novel model, the split at each node is made through a weighted support vector machine classifier with a linear Kernel that minimizes the deviance of the split. We test the model with respect to the usual CART on four public datasets with numerical predictors on three global metrics: Root Mean Squared Error, Mean Absolute Deviation, and R^2. The results of repeated cross-validation show that the novel model can overperform the usual Decision trees.