R Graphical Manual

Browse All

Last data update: 2014.03.03

Data Source

R Release (3.2.3)
CranContrib
BioConductor
All

Data Type

Packages
Functions
Images
Data set

Classification

Results 111 - 120 of 182600 found.
[1] < 7 8 9 10 11 12 13 14 15 16 17 > [18260] Sort:

generic.cv (Package: randomUniformForest) : Generic k-fold cross-validation

Performs k-fold cross-validation 'n' times for any specified algorithm, using two of many metrics(test error, AUC, precision,...)

● Data Source: CranContrib
● Keywords:
● Alias: generic.cv
● 0 images

partialDependenceBetweenPredictors (Package: randomUniformForest) : Partial Dependence between Predictors and effect over Response

Computes partial dependence between two predictors, and their effects on response values.

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

wineQualityRed (Package: randomUniformForest) : Wine Quality Data Set

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.

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

breastCancer (Package: randomUniformForest) : Breast Cancer Wisconsin (Original) Data Set

Original Wisconsin Breast Cancer Database

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

rUniformForest.grow (Package: randomUniformForest) : Add trees to a random Uniform Forest

add more trees to the ensemble model

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

mergeClusters (Package: randomUniformForest) :

merge, on the flight, two adjacent clusters in order to allow better clustering scheme, if needed, and to avoid new computation of the unsupervised mode.

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

biasVarCov (Package: randomUniformForest) : Bias-Variance-Covariance Decomposition

Bias-Variance-Covariance decomposition for Mean Squared Error (MSE) or test error in binary classification, between a response vector and its estimate, over the test sample. For every estimate, based on training examples, MSE on the test sample, between values of the response and values of the estimate, can be decomposed in noise (variance of the response), squared bias (between response and estimate), variance (of the estimate) and covariance (of the response and the estimate). Same decomposition arrives for binary classification with responses in {0, 1}.

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

reSMOTE (Package: randomUniformForest) :

Produce new samples of the minority class by creating synthetic data of the original ones by randomization. After that, the new dataset contains all original data + synthetic data labelled with the minority class. Main argument is derived from Breiman's ideas on the construction of synthetic data for the unsupervised mode of random (Uniform) Forests. The new dataset preserves the distribution of the original dataset covariates(at least with default 'samplingMethod' option).

● Data Source: CranContrib
● Keywords: classes, imbalanced, oversampling, skew
● Alias: reSMOTE
● 0 images

init_values (Package: randomUniformForest) : Training and validation samples from data

Draw training and test samples from data. Samples can be accessed by subsctioting original data or by their own references.

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

fillNA2.randomUniformForest (Package: randomUniformForest) : Missing values imputation by randomUniformForest

Impute missing values using randomUniformForest. Each variable containing missing values is, in turn, considered as a responses vector, where non-missing values are training responses and missing values, responses to predict.

● Data Source: CranContrib
● Keywords:
● Alias: fillNA2.randomUniformForest, rufImpute
● 0 images