Seven state-of-the-art machine learning techniques for estimation of construction costs reinforced-concrete and prestressed concrete bridges are investigated in this paper, including artificial neural networks (ANN) ensembles ANNs, regression tree (random forests, boosted bagged trees), support vector (SVR) method, Gaussian process (GPR). A database design characteristics 181 prestressed-concre...

Accurate prediction and explanation are fundamental objectives of statistical analysis, yet they seldom coincide. Boosted trees are a statistical learning method that attains both of these objectives for regression and classification analyses. They can deal with many types of response variables (numeric, categorical, and censored), loss functions (Gaussian, binomial, Poisson, and robust), and p...

In recent years, substantial improvements were obtained in the effectiveness of data driven algorithms to validate the mapping of items to skills, or the Q-matrix. In the current study we use ensemble algorithms on top of existing Qmatrix refinement algorithms to improve their performance. We combine the boosting technique with a decision tree. The results show that the improvements from both t...

The Spatial Ecology and Epidemiology Group (SEEG) at the University of Oxford is currently carrying out several disease and vector mapping projects. The source code for many of these projects have been and will continue to be made openly available through our GitHub account. This archive includes a description of the R software package ‘seegSDM’, that allows for species distribution modelling u...

This is a brief tutorial to accompany a set of functions that we have written to facilitate fitting BRT (boosted regression tree) models in R . This tutorial is a modified version of the tutorial accompaniying Elith, Leathwick and Hastie’s article in Journal of Animal Ecology. It has been adjusted to match the implementation of these functions in the ’dismo’ package. The gbm* functions in the d...

In this article, we consider nonparametric regression when covariates are measured with error. Estimation is performed using boosted regression trees, with the sum of the trees forming the estimate of the conditional expectation of the response. Both binary and continuous response regression are investigated. An approach to fitting regression trees when covariates are measured with error is des...

Bike sharing system requires prediction of bike usage based on usage history to re-distribute bikes between stations. In this study, original data was collected around Washington D.C. area in 2011 and 2012. Original data is processed by several feature engineering approaches based on analysis and understanding of the data. Ridge linear regression, support vector regression (εSVR), random forest...

Tree ensembles can be interpreted as implicit kernel generators, where the ensuing proximity matrix represents data-driven tree ensemble kernel. Focus of our work is utility based generators that (in conjunction with a regularized linear model) enable learning. We elucidate performance random forest (RF) and gradient boosted (GBT) kernels in comprehensive simulation study comprising continuous ...