Random Uniform Forests for Classification, Regression and Unsupervised Learning
Cluster (or classes) analysis of importance objects.
Cluster observations of a (supervised) randomUniformForest object
Conversion of an unsupervised model into a supervised one
Bootstrapped Prediction Intervals for Ensemble Models
Bias-Variance-Covariance Decomposition
Combine Unsupervised Learning objects
Missing values imputation by randomUniformForest
Generic k-fold cross-validation
Extract a tree from a forest
Variable Importance for random Uniform Forests
Training and validation samples from data
All internal functions
Merge two arbitrary, but adjacent, clusters
Common statistics for a vector (or factor) of predictions and a vector...
Change number of clusters (and clusters shape) on the fly
Partial Dependence between Predictors and effect over Response
Partial Dependence Plots and Models
Partial Importance for random Uniform Forests
Plot a Random Uniform Decision Tree
Post-processing for Regression
Predict method for random Uniform Forests objects
Random Uniform Forests for Classification, Regression and Unsupervised...
Random Uniform Forests for Classification, Regression and Unsupervised...
REplication of a Synthetic Minority Oversampling TEchnique for highly ...
Remove trees from a random Uniform Forest
ROC and precision-recall curves for random Uniform Forests
Random Uniform Forests for Classification and Regression with large da...
Incremental learning for random Uniform Forests
Add trees to a random Uniform Forest
Simulation of Gaussian vector
Split a cluster on the fly
Unsupervised Learning with Random Uniform Forests
Update Unsupervised Learning object
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.