bCI
(Package: randomUniformForest) :
Bootstrapped Prediction Intervals for Ensemble Models
Bootstrapped prediction (and confidence) interval(s) for problems where response vector has a very frequent value (e.g. 0) or for drawing tighter prediction interval (for each response) than those provided by random Uniform Forests.
Combine many random uniform forests on same or different data and/or same and different parameters to obtain one ensemble model. Function acts as the "Reduce" task of MapReduce paradigm. "Map" task has here no defined length, since rUniformForest.combine() is an ensemble of ensembles of any size, parameters or data that can be continuously merged and evaluated as a single model. Native incremental learning is then achieved adding and removing, but never modifying, trees.
Describe what features are the most important for one specifically class (in case of classification) or explain features that are affecting, the most, variability of the response (for regression), either for train or test sample.
Ensemble model for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Unlike Breiman's Random Forests, each tree is grown by sampling, with replacement, a set of variables before splitting each node. Each cut-point is generated randomly, according to the continuous Uniform distribution between two random points of each candidate variable or using its whole current support. Optimal random node is, then, selected among many full random ones by maximizing Information Gain (classification) or minimizing a distance (regression), 'L2' (or 'L1'). Unlike Extremely Randomized Trees, data are either bootstrapped or sub-sampled for each tree. From the theoretical side, Random Uniform Forests are aimed to lower correlation between trees and to offer a deep analysis of variable importance. The unsupervised mode introduces clustering and dimension reduction, using a three-layer engine: dissimilarity matrix, Multidimensional Scaling (or spectral decomposition) and k-means (or hierarchical clustering). From the practical side, Random Uniform Forests are designed to provide a complete analysis of (un)supervised problems and to allow native distributed and incremental learning.
Ensemble model for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Unlike Breiman's Random Forests, each tree is grown by sampling, with replacement, a set of variables before splitting each node. Each cut-point is generated randomly, according to the continuous Uniform distribution between two random points of each candidate variable or using its whole current support. Optimal random node is, then, selected among many full random ones by maximizing Information Gain (classification) or minimizing a distance (regression), 'L2' (or 'L1'). Unlike Extremely Randomized Trees, data are either bootstrapped or sub-sampled for each tree. From the theoretical side, Random Uniform Forests are aimed to lower correlation between trees and to offer a deep analysis of variable importance. The unsupervised mode introduces clustering and dimension reduction, using a three-layer engine: dissimilarity matrix, Multidimensional Scaling (or Spectral decomposition) and k-means (or hierarchical clustering). From the practical side, Random Uniform Forests are designed to provide a complete analysis of (un)supervised problems and to allow native distributed and incremental learning.
Unsupervised mode of Random Uniform Forests is designed to provide, in all cases, clustering, dimension reduction, easy visualization, deep variable importance, relations between observations, variables and clusters. It also comes with two specific points: easy assessment (cluster analysis) and dynamic clustering, allowing to change on-the-fly any clustering shape. A three-layer engine is used: dissimilarity matrix, Multidimensional Scaling (MDS) or Spectral decomposition, and k-means or hierarchical clustering. The unsupervised mode does not require the number of clusters to be known, thanks to the gap statistic, and inherits of main algorithmic properties of the supervised mode, allowing (almost) any type of variable.
Modify on the fly the number of clusters in the unsupervised mode of Random Uniform Forests. Once the unsupervised model is built, one can change the clustering scheme, adding or merging clusters without computing again the model. Average silhouette coefficient or variance between clusters are automatically adjusted in order to assess the new scheme.
Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests.
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