caret v6.0-35
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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 | |
| bag.default | A General Framework For Bagging | |
| confusionMatrix | Create a confusion matrix | |
| predict.knn3 | Predictions from k-Nearest Neighbors | |
| plotObsVsPred | Plot Observed versus Predicted Results in Regression and Classification Models | |
| as.table.confusionMatrix | Save Confusion Table Results | |
| plsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis | |
| BoxCoxTrans.default | Box-Cox and Exponential Transformations | |
| nearZeroVar | Identification of near zero variance predictors | |
| plot.rfe | Plot RFE Performance Profiles | |
| plotClassProbs | Plot Predicted Probabilities in Classification Models | |
| segmentationData | Cell Body Segmentation | |
| spatialSign | Compute the multivariate spatial sign | |
| resampleSummary | Summary of resampled performance estimates | |
| predictors | List predictors used in the model | |
| pottery | Pottery from Pre-Classical Sites in Italy | |
| avNNet.default | Neural Networks Using Model Averaging | |
| nullModel | Fit a simple, non-informative model | |
| filterVarImp | Calculation of filter-based variable importance | |
| caretFuncs | Backwards Feature Selection Helper Functions | |
| confusionMatrix.train | Estimate a Resampled Confusion Matrix | |
| xyplot.resamples | Lattice Functions for Visualizing Resampling Results | |
| lattice.rfe | Lattice functions for plotting resampling results of recursive feature selection | |
| twoClassSim | Simulation Functions | |
| dotPlot | Create a dotplot of variable importance values | |
| downSample | Down- and Up-Sampling Imbalanced Data | |
| print.train | Print Method for the train Class | |
| BloodBrain | Blood Brain Barrier Data | |
| format.bagEarth | Format 'bagEarth' objects | |
| resampleHist | Plot the resampling distribution of the model statistics | |
| oneSE | Selecting tuning Parameters | |
| plot.train | Plot Method for the train Class | |
| histogram.train | Lattice functions for plotting resampling results | |
| classDist | Compute and predict the distances to class centroids | |
| sbf | Selection By Filtering (SBF) | |
| GermanCredit | German Credit Data | |
| predict.train | Extract predictions and class probabilities from train objects | |
| pcaNNet.default | Neural Networks with a Principal Component Step | |
| postResample | Calculates performance across resamples | |
| panel.needle | Needle Plot Lattice Panel | |
| modelLookup | Tools for Models Available in train | |
| predict.knnreg | Predictions from k-Nearest Neighbors Regression Model | |
| predict.bagEarth | Predicted values based on bagged Earth and FDA models | |
| cox2 | COX-2 Activity Data | |
| icr.formula | Independent Component Regression | |
| rfe | Backwards Feature Selection | |
| dhfr | Dihydrofolate Reductase Inhibitors Data | |
| findLinearCombos | Determine linear combinations in a matrix | |
| knn3 | k-Nearest Neighbour Classification | |
| calibration | Probability Calibration Plot | |
| maxDissim | Maximum Dissimilarity Sampling | |
| dotplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences | |
| print.confusionMatrix | Print method for confusionMatrix | |
| featurePlot | Wrapper for Lattice Plotting of Predictor Variables | |
| rfeControl | Controlling the Feature Selection Algorithms | |
| preProcess | Pre-Processing of Predictors | |
| train_model_list | A List of Available Models in train | |
| diff.resamples | Inferential Assessments About Model Performance | |
| findCorrelation | Determine highly correlated variables | |
| lift | Lift Plot | |
| resamples | Collation and Visualization of Resampling Results | |
| mdrr | Multidrug Resistance Reversal (MDRR) Agent Data | |
| prcomp.resamples | Principal Components Analysis of Resampling Results | |
| tecator | Fat, Water and Protein Content of Meat Samples | |
| varImp | Calculation of variable importance for regression and classification models | |
| summary.bagEarth | Summarize a bagged earth or FDA fit | |
| caretSBF | Selection By Filtering (SBF) Helper Functions | |
| panel.lift2 | Lattice Panel Functions for Lift Plots | |
| plot.varImp.train | Plotting variable importance measures | |
| cars | Kelly Blue Book resale data for 2005 model year GM cars | |
| bagEarth | Bagged Earth | |
| oil | Fatty acid composition of commercial oils | |
| sbfControl | Control Object for Selection By Filtering (SBF) | |
| trainControl | Control parameters for train | |
| update.train | Update or Re-fit a Model | |
| bagFDA | Bagged FDA | |
| sensitivity | Calculate sensitivity, specificity and predictive values | |
| knnreg | k-Nearest Neighbour Regression | |
| dummyVars | Create A Full Set of Dummy Variables | |
| caret-internal | Internal Functions | |
| createDataPartition | Data Splitting functions | |
| train | Fit Predictive Models over Different Tuning Parameters | |
| No Results! | ||
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Details
| Date | 2014-08-22 |
| URL | http://caret.r-forge.r-project.org/ |
| License | GPL-2 |
| Packaged | 2014-08-24 20:11:07 UTC; kuhna03 |
| NeedsCompilation | yes |
| Repository | CRAN |
| Date/Publication | 2014-08-24 23:07:35 |
| depends | base (>= 2.10) , ggplot2 , lattice (>= 0.20) , R (>= 2.10) , stats |
| imports | BradleyTerry2 , car , foreach , methods , nlme , plyr , reshape2 |
| suggests | e1071 , earth (>= 2.2-3) , ellipse , fastICA , gam , ipred , kernlab , klaR , MASS , mda , mgcv , mlbench , nnet , party (>= 0.9-99992) , pls , pROC , proxy , randomForest , RANN , spls |
| Contributors | the Core team, Max Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer |
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