A comparison among interpretative proposals for Random Forests

A Comparison of Logistic Regression to Random Forests For

Asymptotic Theory for Random Forests

Random forests have proven to be reliable predictive algorithms in many application areas. Not much is known, however, about the statistical properties of random forests. Several authors have established conditions under which their predictions are consistent, but these results do not provide practical estimates of random forest errors. In this paper, we analyze a random forest model based on s...

Random Forests for Big Data

Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include data streams and data heterogeneity. Recently some statistical methods have been adapted to process Big Data, like linear regression models, clustering methods and bootstrapping schemes. Based o...

Random Forests for CUDA GPUs

Context. Machine Learning is a complex and resource consuming process that requires a lot of computing power. With the constant growth of information, the need for efficient algorithms with high performance is increasing. Today's commodity graphics cards are parallel multi processors with high computing capacity at an attractive price and are usually pre-installed in new PCs. The graphics cards...

Proposals for a Failure Criterion Applicable to Discontinuous Rocks

The existing failure criteria to evaluate discontinuous rocks are predominatly based on the relations between principal stresses at failure. None of the criteria has a direct reference to the magnitude of deformation as a major parameter in rock Several reasons are identified indicating that the selection of peak stress as a major parameter, which is obtained through the evaluation of a failure...