caret v4.60
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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 | |
| bagEarth | Bagged Earth | |
| bagFDA | Bagged FDA | |
| oil | Fatty acid composition of commercial oils | |
| knn3 | k-Nearest Neighbour Classification | |
| predict.bagEarth | Predicted values based on bagged Earth and FDA models | |
| pottery | Pottery from Pre-Classical Sites in Italy | |
| rfeControl | Controlling the Feature Selection Algorithms | |
| dotPlot | Create a dotplot of variable importance values | |
| confusionMatrix | Create a confusion matrix | |
| predict.train | Extract predictions and class probabilities from train objects | |
| histogram.train | Lattice functions for plotting resampling results | |
| nullModel | Fit a simple, non-informative model | |
| plot.varImp.train | Plotting variable importance measures | |
| createDataPartition | Data Splitting functions | |
| resamples | Collation and Visualization of Resampling Results | |
| caretFuncs | Backwards Feature Selection Helper Functions | |
| caretSBF | Selection By Filtering (SBF) Helper Functions | |
| sbfControl | Control Object for Selection By Filtering (SBF) | |
| caret-internal | Internal Functions | |
| knnreg | k-Nearest Neighbour Regression | |
| print.confusionMatrix | Print method for confusionMatrix | |
| createGrid | Tuning Parameter Grid | |
| GermanCredit | German Credit Data | |
| findCorrelation | Determine highly correlated variables | |
| findLinearCombos | Determine linear combinations in a matrix | |
| Alternate Affy Gene Expression Summary Methods. | Generate Expression Values from Probes | |
| lattice.rfe | Lattice functions for plotting resampling results of recursive feature selection | |
| train | Fit Predictive Models over Different Tuning Parameters | |
| format.bagEarth | Format 'bagEarth' objects | |
| modelLookup | Descriptions Of Models Available in train() | |
| nearZeroVar | Identification of near zero variance predictors | |
| plot.train | Plot Method for the train Class | |
| normalize2Reference | Quantile Normalize Columns of a Matrix Based on a Reference Distribution | |
| summary.bagEarth | Summarize a bagged earth or FDA fit | |
| as.table.confusionMatrix | Save Confusion Table Results | |
| diff.resamples | Inferential Assessments About Model Performance | |
| mdrr | Multidrug Resistance Reversal (MDRR) Agent Data | |
| normalize.AffyBatch.normalize2Reference | Quantile Normalization to a Reference Distribution | |
| oneSE | Selecting tuning Parameters | |
| spatialSign | Compute the multivariate spatial sign | |
| BloodBrain | Blood Brain Barrier Data | |
| cars | Kelly Blue Book resale data for 2005 model year GM cars | |
| dhfr | Dihydrofolate Reductase Inhibitors Data | |
| filterVarImp | Calculation of filter-based variable importance | |
| featurePlot | Wrapper for Lattice Plotting of Predictor Variables | |
| pcaNNet.default | Neural Networks with a Principal Component Step | |
| plotClassProbs | Plot Predicted Probabilities in Classification Models | |
| plsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis | |
| preProcess | Pre-Processing of Predictors | |
| print.train | Print Method for the train Class | |
| sensitivity | Calculate sensitivity, specificity and predictive values | |
| trainControl | Control parameters for train | |
| classDist | Compute and predict the distances to class centroids | |
| applyProcessing | Data Processing on Predictor Variables (Deprecated) | |
| icr.formula | Independent Component Regression | |
| dotplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences | |
| xyplot.resamples | Lattice Functions for Visualizing Resampling Results | |
| maxDissim | Maximum Dissimilarity Sampling | |
| predict.knnreg | Predictions from k-Nearest Neighbors Regression Model | |
| sbf | Selection By Filtering (SBF) | |
| resampleHist | Plot the resampling distribution of the model statistics | |
| varImp | Calculation of variable importance for regression and classification models | |
| bag.default | A General Framework For Bagging | |
| prcomp.resamples | Principal Components Analysis of Resampling Results | |
| predictors | List predictors used in the model | |
| panel.needle | Needle Plot Lattice Panel | |
| aucRoc | Compute the area under an ROC curve | |
| cox2 | COX-2 Activity Data | |
| postResample | Calculates performance across resamples | |
| plotObsVsPred | Plot Observed versus Predicted Results in Regression and Classification Models | |
| predict.knn3 | Predictions from k-Nearest Neighbors | |
| rfe | Backwards Feature Selection | |
| resampleSummary | Summary of resampled performance estimates | |
| roc | Compute the points for an ROC curve | |
| tecator | Fat, Water and Protein Content of Meat Samples | |
| No Results! | ||
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Details
| Date | 2010-09-02 |
| URL | http://caret.r-forge.r-project.org/ |
| License | GPL-2 |
| Packaged | 2010-09-02 20:52:46 UTC; kuhna03 |
| Repository | CRAN |
| Date/Publication | 2010-09-03 07:31:33 |
| suggests | ada , affy , 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 , quantregForest , randomForest , 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 , R (>= 2.5.1) , reshape , stats |
| Contributors | Max Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt |
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