Abstract Decision trees (DT) are highly famous in machine learning and usually acquire state-of-the-art performance. Despite that, well-known variants like CART, ID3, random forest, boosted miss a probabilistic version that encodes prior assumptions about tree structures shares statistical strength between node parameters. Existing work on Bayesian DT depends Markov Chain Monte Carlo (MCMC), wh...

In this paper, we propose a piecewise linear decision tree and its generalized form, namely the (G)PWL-DT, which introduces linearity overcomes discontinuity of existing constant trees (PWC-DT). The proposed (G)PWL-DT inherits basic topology interpretability by recursively partitioning domain into subregions, are represented leaf nodes. Rather than indicator function, employs rectifier units (R...

It has been observed that traditional decision trees produce poor probability estimates. In many applications, however, a probability estimation tree (PET) with accurate probability estimates is desirable. Some researchers ascribe the poor probability estimates of decision trees to the decision tree learning algorithms. To our observation, however, the representation also plays an important rol...