caret v4.75
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by Max Kuhn
Classification and Regression Training
Misc functions for training and plotting classification
and regression models
Functions in caret
Name | Description | |
aucRoc | Compute the area under an ROC curve | |
predict.bagEarth | Predicted values based on bagged Earth and FDA models | |
filterVarImp | Calculation of filter-based variable importance | |
nearZeroVar | Identification of near zero variance predictors | |
dotplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences | |
as.table.confusionMatrix | Save Confusion Table Results | |
dhfr | Dihydrofolate Reductase Inhibitors Data | |
panel.needle | Needle Plot Lattice Panel | |
bagEarth | Bagged Earth | |
bagFDA | Bagged FDA | |
plotClassProbs | Plot Predicted Probabilities in Classification Models | |
nullModel | Fit a simple, non-informative model | |
dummyVars | Create A Full Set of Dummy Variables | |
pottery | Pottery from Pre-Classical Sites in Italy | |
findLinearCombos | Determine linear combinations in a matrix | |
preProcess | Pre-Processing of Predictors | |
BloodBrain | Blood Brain Barrier Data | |
modelLookup | Descriptions Of Models Available in train() | |
rfe | Backwards Feature Selection | |
createDataPartition | Data Splitting functions | |
lattice.rfe | Lattice functions for plotting resampling results of recursive feature selection | |
resampleHist | Plot the resampling distribution of the model statistics | |
resamples | Collation and Visualization of Resampling Results | |
maxDissim | Maximum Dissimilarity Sampling | |
cox2 | COX-2 Activity Data | |
postResample | Calculates performance across resamples | |
featurePlot | Wrapper for Lattice Plotting of Predictor Variables | |
print.confusionMatrix | Print method for confusionMatrix | |
createGrid | Tuning Parameter Grid | |
plsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis | |
predict.knn3 | Predictions from k-Nearest Neighbors | |
normalize.AffyBatch.normalize2Reference | Quantile Normalization to a Reference Distribution | |
predict.knnreg | Predictions from k-Nearest Neighbors Regression Model | |
plot.varImp.train | Plotting variable importance measures | |
confusionMatrix | Create a confusion matrix | |
applyProcessing | Data Processing on Predictor Variables (Deprecated) | |
prcomp.resamples | Principal Components Analysis of Resampling Results | |
xyplot.resamples | Lattice Functions for Visualizing Resampling Results | |
knn3 | k-Nearest Neighbour Classification | |
plotObsVsPred | Plot Observed versus Predicted Results in Regression and Classification Models | |
Alternate Affy Gene Expression Summary Methods. | Generate Expression Values from Probes | |
GermanCredit | German Credit Data | |
predict.train | Extract predictions and class probabilities from train objects | |
classDist | Compute and predict the distances to class centroids | |
diff.resamples | Inferential Assessments About Model Performance | |
bag.default | A General Framework For Bagging | |
dotPlot | Create a dotplot of variable importance values | |
normalize2Reference | Quantile Normalize Columns of a Matrix Based on a Reference Distribution | |
mdrr | Multidrug Resistance Reversal (MDRR) Agent Data | |
pcaNNet.default | Neural Networks with a Principal Component Step | |
cars | Kelly Blue Book resale data for 2005 model year GM cars | |
caret-internal | Internal Functions | |
oil | Fatty acid composition of commercial oils | |
print.train | Print Method for the train Class | |
caretFuncs | Backwards Feature Selection Helper Functions | |
knnreg | k-Nearest Neighbour Regression | |
caretSBF | Selection By Filtering (SBF) Helper Functions | |
format.bagEarth | Format 'bagEarth' objects | |
findCorrelation | Determine highly correlated variables | |
icr.formula | Independent Component Regression | |
resampleSummary | Summary of resampled performance estimates | |
predictors | List predictors used in the model | |
trainControl | Control parameters for train | |
sbf | Selection By Filtering (SBF) | |
tecator | Fat, Water and Protein Content of Meat Samples | |
sbfControl | Control Object for Selection By Filtering (SBF) | |
summary.bagEarth | Summarize a bagged earth or FDA fit | |
train | Fit Predictive Models over Different Tuning Parameters | |
segmentationData | Cell Body Segmentation | |
sensitivity | Calculate sensitivity, specificity and predictive values | |
spatialSign | Compute the multivariate spatial sign | |
oneSE | Selecting tuning Parameters | |
varImp | Calculation of variable importance for regression and classification models | |
rfeControl | Controlling the Feature Selection Algorithms | |
roc | Compute the points for an ROC curve | |
histogram.train | Lattice functions for plotting resampling results | |
plot.train | Plot Method for the train Class | |
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Details
Date | 2010-12-30 |
URL | http://caret.r-forge.r-project.org/ |
License | GPL-2 |
Packaged | 2011-01-01 13:20:48 UTC; kuhna03 |
Repository | CRAN |
Date/Publication | 2011-01-03 12:34:14 |
suggests | ada , affy , Boruta , caTools , class , e1071 , earth (>= 2.2-3) , elasticnet , ellipse , fastICA , foba , foreach , gam , GAMens (>= 1.1.1) , gbm , glmnet , gpls , grid , hda , HDclassif , ipred , kernlab , klaR , lars , LogicForest , logicFS , LogicReg , MASS , mboost , mda , mgcv , mlbench , neuralnet , nnet , nodeHarvest , pamr , partDSA , party , penalized , pls , proxy , qrnn , quantregForest , randomForest , RANN , rda , relaxo , rocc , rpart , rrcov , RWeka (>= 0.4-1) , sda , SDDA , sparseLDA (>= 0.1-1) , spls , stepPlr , superpc , vbmp |
depends | base (>= 2.5.1) , lattice , plyr , R (>= 2.5.1) , reshape , stats |
Contributors | Max Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt |
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