R Graphical Manual

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Last data update: 2014.03.03

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Results 121 - 130 of 182600 found.
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plotTree (Package: randomUniformForest) : Plot a Random Uniform Decision Tree

plot the tree structure, showing nodes, variables, cut-points and predictions.

● Data Source: CranContrib
● Keywords:
● Alias: plotTree
● 0 images

clusteringObservations (Package: randomUniformForest) :

Provide a clustering scheme for observations (in training or test sample) in a (supervised) randomUniformForest object. Observations are clustered using their labels or predictions. The rest of the process is the same than in the unsupervised case. clusteringObservations() may be needed when one wants to know how the classifier can have some troubles in the prediction task, especially when a link between features and decision rule has to be made.

● Data Source: CranContrib
● Keywords: clustering, dimension, learning, reduction, unsupervised
● Alias: clusteringObservations
● 0 images

update.unsupervised (Package: randomUniformForest) :

Update unsupervised learning object with new data in order to achieve incremental learning. New MDS (or spectral) points are predicted with new data and learning of MDS (spectral) points of the former unsupervised object. Note that new data are expected to have the same distribution than previous ones.

● Data Source: CranContrib
● Keywords: clustering, dimension, learning, reduction, unsupervised
● Alias: update, update.unsupervised
● 0 images

partialDependenceOverResponses (Package: randomUniformForest) : Partial Dependence Plots and Models

Computes partial dependence between expected conditional response and all values of its target feature, knowing the distribution of all others features in the data (e.g. marginal effect of the target feature over the response)

● Data Source: CranContrib
● Keywords:
● Alias: partialDependenceOverResponses
● 0 images

combineUnsupervised (Package: randomUniformForest) :

Combine unsupervised learning objects in order to achieve incremental learning. Only the MDS (spectral) points are used before calling a clustering algorithm on all. Note that the function is currently highly experimental with a lack of applications.

● Data Source: CranContrib
● Keywords: clustering, dimension, learning, reduction, unsupervised
● Alias: combineUnsupervised
● 0 images

clusterAnalysis (Package: randomUniformForest) :

Provides a full analysis of clustered objects in a compact and granular representation. More precisely, observations, features and clusters are analysed in a same scheme, leading to unify and interpret all results of the unsupervised mode in one way. The function prints at most 5 tables and is designed to be an extension of importance object and also works in the supervised mode.

● Data Source: CranContrib
● Keywords:
● Alias: clusterAnalysis
● 0 images

rUniformForest.big (Package: randomUniformForest) : Random Uniform Forests for Classification and Regression with large data sets

Implements random uniform forests for data sets that are too large too fit in physical memory but enough too fit in virtual memory. data set is randomly (or not) cut in many sub-samples and each one is processed, getting many base (but ensemble) models per sub-sample. At the end, all base models are combined to obtain one ensemble of ensembles model. If data can not reside in physical memory, but can reside in virtual memory (physical memory + swap file) then consider R packages 'bigmemory', 'data.table' of 'ff' to load data. To save memory (and computing time), subsamples of data (e.g. using an Hadoop environment like) are suited, computing then one forest per sub-sample and combining all trees from all forests using rUniformForest.combine. Note that rUniformForest.big( ) is first designed to compute large files on a small computer, at the expense of accuracy. But, in case of a shifting distribution, model may be more robust than the standard one (at least for regression).

● Data Source: CranContrib
● Keywords:
● Alias: rUniformForest.big
● 0 images

getTree.randomUniformForest (Package: randomUniformForest) : Extract a tree from a forest

get the structure of a tree from a randomUniformForest object.

● Data Source: CranContrib
● Keywords:
● Alias: getTree, getTree.randomUniformForest
● 0 images

roc.curve (Package: randomUniformForest) : ROC and precision-recall curves for random Uniform Forests

plot ROC and precision-recall curves for objects of class randomUniformForest and compute F-beta score. It also works for any other model that provides predicted labels (but only for ROC curve).

● Data Source: CranContrib
● Keywords:
● Alias: roc.curve
● 0 images

model.stats (Package: randomUniformForest) :

Given a vector of predictions and a vector of responses, provide some statistics and plots like AUC, AUPR, confusion matrix, F1-score, geometric mean, residuals, mean squared and mean absolute error.

● Data Source: CranContrib
● Keywords:
● Alias: model.stats
● 0 images