Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Each tree is grown by sampling, with replacement, a set of variables at each node. Each cut-point is generated randomly, according to the continuous Uniform distribution. For each tree, data are either bootstrapped or subsampled. The unsupervised mode introduces clustering, dimension reduction and variable importance, using a three-layer engine. Random Uniform Forests are mainly aimed to lower correlation between trees (or trees residuals), to provide a deep analysis of variable importance and to allow native distributed and incremental learning.
|Date of publication||2015-02-16 21:29:00|
|Maintainer||Saip Ciss <email@example.com>|
|License||BSD_3_clause + file LICENSE|
as.supervised: Conversion of an unsupervised model into a supervised one
autoMPG: Auto MPG Data Set
bCI: Bootstrapped Prediction Intervals for Ensemble Models
biasVarCov: Bias-Variance-Covariance Decomposition
breastCancer: Breast Cancer Wisconsin (Original) Data Set
carEvaluation: Car Evaluation Data Set
clusterAnalysis: Cluster (or classes) analysis of importance objects.
clusteringObservations: Cluster observations of a (supervised) randomUniformForest...
combineUnsupervised: Combine Unsupervised Learning objects
ConcreteCompressiveStrength: Concrete Compressive Strength Data Set
fillNA2.randomUniformForest: Missing values imputation by randomUniformForest
generic.cv: Generic k-fold cross-validation
getTree.randomUniformForest: Extract a tree from a forest
importance.randomUniformForest: Variable Importance for random Uniform Forests
init_values: Training and validation samples from data
internalFunctions: All internal functions
mergeClusters: Merge two arbitrary, but adjacent, clusters
model.stats: Common statistics for a vector (or factor) of predictions and...
modifyClusters: Change number of clusters (and clusters shape) on the fly
partialDependenceBetweenPredictors: Partial Dependence between Predictors and effect over...
partialDependenceOverResponses: Partial Dependence Plots and Models
partialImportance: Partial Importance for random Uniform Forests
plotTree: Plot a Random Uniform Decision Tree
postProcessingVotes: Post-processing for Regression
predict.randomUniformForest: Predict method for random Uniform Forests objects
randomUniformForest: Random Uniform Forests for Classification, Regression and...
randomUniformForest-package: Random Uniform Forests for Classification, Regression and...
reSMOTE: REplication of a Synthetic Minority Oversampling TEchnique...
rm.trees: Remove trees from a random Uniform Forest
roc.curve: ROC and precision-recall curves for random Uniform Forests
rUniformForest.big: Random Uniform Forests for Classification and Regression with...
rUniformForest.combine: Incremental learning for random Uniform Forests
rUniformForest.grow: Add trees to a random Uniform Forest
simulationData: Simulation of Gaussian vector
splitClusters: Split a cluster on the fly
unsupervised.randomUniformForest: Unsupervised Learning with Random Uniform Forests
update.unsupervised: Update Unsupervised Learning object
wineQualityRed: Wine Quality Data Set