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...

Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve competitive predictive performance and are computationally efficient to train and test, making them excellent candidates for real-world prediction tasks. The most popular random forest variants (such as ...

In May 2010 Yahoo! Inc. hosted the Learning to Rank Challenge. This paper summarizes the approach by the highly placed team Washington University in St. Louis. We investigate Random Forests (RF) as a low-cost alternative algorithm to Gradient Boosted Regression Trees (GBRT) (the de facto standard of web-search ranking). We demonstrate that it yields surprisingly accurate ranking results — compa...

Ensemble learning techniques and in particular Random Forests have been one of the most successful machine learning approaches of the last decade. Despite their success, there exist barely suitable visualizations of Random Forests, which allow a fast and accurate understanding of how well they perform a certain task and what leads to this performance. This paper proposes an exemplar-driven visu...

We introduce a broad family of decision trees, Composite Trees, whose leaf classifiers are selected out of a hypothesis set composed of p subfamilies with different complexities. We prove new data-dependent learning guarantees for this family in the multi-class setting. These learning bounds provide a quantitative guidance for the choice of the hypotheses at each leaf. Remarkably, they depend o...

In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit we extract the residuals and then fit another random forest to these residuals. We call the sum of these two random forests a one-step boosted forest. We have shown with simulated and real data that the one-step boosted forest h...

7 Feature-space modelling 16 7.1 Theory of linear models . . . . . . . . . . . . . . . . . . . . . . 16 7.1.1 * Least-squares solution of the linear model . . . . . . . 17 7.2 Continuous response, continuous predictor . . . . . . . . . . . . 18 7.3 Continuous response, categorical predictor . . . . . . . . . . . . 23 7.4 * Multivariate linear models . . . . . . . . . . . . . . . . . . . . 25 7....

This paper presents different scalable methods to predict time series of very long length such as time series with a high sampling frequency. The Apache Spark framework for distributed computing is proposed in order to achieve the scalability of the methods. Namely, the existing MLlib machine learning library from Spark has been used. Since MLlib does not support multivariate regression, the fo...