Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

A decision tree is a predictive model that recursively partitions the covariate's space into
subspaces such that each subspace constitutes a basis for a different prediction function …

Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …

We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural
networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging …

The idea of ensemble methodology is to build a predictive model by integrating multiple
models. It is well-known that ensemble methods can be used for improving prediction …

Witten and Frank's textbook was one of two books that 1 used for a data mining class in the
Fall of 2001. The book covers all major methods of data mining that produce a knowledge …

Decision trees have become one of the most powerful and popular approaches in
knowledge discovery and data mining; it is the science of exploring large and complex …

We empirically evaluate several state-of-the-art methods for constructing ensembles of
heterogeneous classifiers with stacking and show that they perform (at best) comparably to …

Data Mining Page 1 Data Mining Practical Machine Learning Tools and Techniques Third
Edition Ian H. Witten Eibe Frank Mark A. Hall ELSEVIER AMSTERDAM • BOSTON • …

The conformal predictions framework is a recent development in machine learning that can
associate a reliable measure of confidence with a prediction in any real-world pattern …