Hedonic pricing models attempt to model a relationship between object attributes and the object’s price. Traditional hedonic pricing models are often parametric models that suffer from misspecification. In this paper we create these models by means of boosted CART models. The method is explained in detail and applied to various datasets. Empirically, we find substantial reduction of errors on o...

Decision trees effectively represent the sparse, high dimensional and noisy nature of chemical data from experiments. Having learned a function from this data, we may want to thereafter optimize the function, e.g., picking the best chemical process catalyst. In this way, we may repurpose legacy predictive models. This work studies a large-scale, industrially-relevant mixed-integer quadratic opt...

With the resurgence of tick-borne diseases such as Lyme disease and the emergence of new pathogens such as Powassan virus, understanding what distinguishes vector from non-vector species, and predicting undiscovered tick vectors is an important step towards mitigating human disease risk. We apply generalized boosted regression to interrogate over 90 features for over 240 species of Ixodes ticks...

We propose a new approach for search tree exploration in the context of combinatorial optimization, specifically Mixed Integer Programming (MIP), that is based on UCT, an algorithm for the multi-armed bandit problem designed for balancing exploration and exploitation in an online fashion. UCT has recently been highly successful in game tree search. We discuss the differences that arise when UCT...

Decision trees, either classification or regression trees, are especially attractive type of models for three main reasons. First, they have an intuitive representation, the resulting model is easy to understand and assimilate by humans [BFOS84]. Second, the decision trees are nonparametric models, no intervention being required from the user, and thus they are very suited for exploratory knowl...

This paper considers the removal of low-frequency trend contributions from turbulence intensity values at sites for which only 10-min statistics in wind speed are available. It is proposed problem be reformulated as a direct regression task, solvable using machine learning techniques conjunction with training data formed measurements underlying (non-averaged) Once trained, models can de-trend h...

Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (AN...

Because their distribution usually depends on the presence of more than one species, modelling zoonotic diseases in humans differs from modelling individual species distribution even though the data are similar in nature. Three approaches can be used to model spatial distributions recorded by points: based on presence/absence, presence/available or presence data. Here, we compared one or two of...

Accurate estimation of site productivity is essential for forest projections and scenario modelling. We present evaluate models to predict index (SI) whether a productive (potential total stem volume production ≥ 1 m 3 ·ha −1 ·year ) in wall-to-wall high-resolution (16 × 16 m) SI map Norway. investigate remotely sensed data improve predictions. also study the advantages disadvantages using boos...

OBJECTIVE Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, rac...