# caret v6.0-77

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## Classification and Regression Training

Misc functions for training and plotting classification and
regression models.

## Functions in caret

Name | Description | |

bagFDA | Bagged FDA | |

calibration | Probability Calibration Plot | |

as.matrix.confusionMatrix | Confusion matrix as a table | |

avNNet | Neural Networks Using Model Averaging | |

GermanCredit | German Credit Data | |

Sacramento | Sacramento CA Home Prices | |

bag | A General Framework For Bagging | |

bagEarth | Bagged Earth | |

BloodBrain | Blood Brain Barrier Data | |

BoxCoxTrans | Box-Cox and Exponential Transformations | |

confusionMatrix.train | Estimate a Resampled Confusion Matrix | |

cox2 | COX-2 Activity Data | |

dhfr | Dihydrofolate Reductase Inhibitors Data | |

diff.resamples | Inferential Assessments About Model Performance | |

caretSBF | Selection By Filtering (SBF) Helper Functions | |

cars | Kelly Blue Book resale data for 2005 model year GM cars | |

dotPlot | Create a dotplot of variable importance values | |

dotplot.diff.resamples | Lattice Functions for Visualizing Resampling Differences | |

createDataPartition | Data Splitting functions | |

densityplot.rfe | Lattice functions for plotting resampling results of recursive feature selection | |

findCorrelation | Determine highly correlated variables | |

findLinearCombos | Determine linear combinations in a matrix | |

gafs_initial | Ancillary genetic algorithm functions | |

index2vec | Convert indicies to a binary vector | |

knn3 | k-Nearest Neighbour Classification | |

mdrr | Multidrug Resistance Reversal (MDRR) Agent Data | |

modelLookup | Tools for Models Available in train | |

plsda | Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis | |

classDist | Compute and predict the distances to class centroids | |

confusionMatrix | Create a confusion matrix | |

featurePlot | Wrapper for Lattice Plotting of Predictor Variables | |

filterVarImp | Calculation of filter-based variable importance | |

format.bagEarth | Format 'bagEarth' objects | |

gafs.default | Genetic algorithm feature selection | |

nullModel | Fit a simple, non-informative model | |

oil | Fatty acid composition of commercial oils | |

panel.needle | Needle Plot Lattice Panel | |

pcaNNet | Neural Networks with a Principal Component Step | |

extractPrediction | Extract predictions and class probabilities from train objects | |

resamples | Collation and Visualization of Resampling Results | |

rfe | Backwards Feature Selection | |

getSamplingInfo | Get sampling info from a train model | |

train_model_list | A List of Available Models in train | |

nearZeroVar | Identification of near zero variance predictors | |

predict.knnreg | Predictions from k-Nearest Neighbors Regression Model | |

defaultSummary | Calculates performance across resamples | |

predictors | List predictors used in the model | |

print.confusionMatrix | Print method for confusionMatrix | |

resampleHist | Plot the resampling distribution of the model statistics | |

resampleSummary | Summary of resampled performance estimates | |

summary.bagEarth | Summarize a bagged earth or FDA fit | |

tecator | Fat, Water and Protein Content of Meat Samples | |

varImp | Calculation of variable importance for regression and classification models | |

varImp.gafs | Variable importances for GAs and SAs | |

scat | Morphometric Data on Scat | |

segmentationData | Cell Body Segmentation | |

caret-internal | Internal Functions | |

pickSizeBest | Backwards Feature Selection Helper Functions | |

downSample | Down- and Up-Sampling Imbalanced Data | |

dummyVars | Create A Full Set of Dummy Variables | |

knnreg | k-Nearest Neighbour Regression | |

ggplot.train | Plot Method for the train Class | |

plot.varImp.train | Plotting variable importance measures | |

predict.gafs | Predict new samples | |

predict.knn3 | Predictions from k-Nearest Neighbors | |

histogram.train | Lattice functions for plotting resampling results | |

icr.formula | Independent Component Regression | |

oneSE | Selecting tuning Parameters | |

learing_curve_dat | Create Data to Plot a Learning Curve | |

lift | Lift Plot | |

maxDissim | Maximum Dissimilarity Sampling | |

plot.gafs | Plot Method for the gafs and safs Classes | |

panel.lift2 | Lattice Panel Functions for Lift Plots | |

plotClassProbs | Plot Predicted Probabilities in Classification Models | |

plotObsVsPred | Plot Observed versus Predicted Results in Regression and Classification Models | |

print.train | Print Method for the train Class | |

recall | Calculate recall, precision and F values | |

negPredValue | Calculate sensitivity, specificity and predictive values | |

spatialSign | Compute the multivariate spatial sign | |

preProcess | Pre-Processing of Predictors | |

predict.bagEarth | Predicted values based on bagged Earth and FDA models | |

gafsControl | Control parameters for GA and SA feature selection | |

safs_initial | Ancillary simulated annealing functions | |

sbf | Selection By Filtering (SBF) | |

sbfControl | Control Object for Selection By Filtering (SBF) | |

trainControl | Control parameters for train | |

SLC14_1 | Simulation Functions | |

ggplot.rfe | Plot RFE Performance Profiles | |

pottery | Pottery from Pre-Classical Sites in Italy | |

prcomp.resamples | Principal Components Analysis of Resampling Results | |

rfeControl | Controlling the Feature Selection Algorithms | |

safs | Simulated annealing feature selection | |

thresholder | Generate Data to Choose a Probability Threshold | |

train | Fit Predictive Models over Different Tuning Parameters | |

update.safs | Update or Re-fit a SA or GA Model | |

var_seq | Sequences of Variables for Tuning | |

xyplot.resamples | Lattice Functions for Visualizing Resampling Results | |

update.train | Update or Re-fit a Model | |

No Results! |

## Vignettes of caret

Name | ||

algorithm.tex | ||

caret.Rnw | ||

train_algo.pdf | ||

No Results! |

## Last month downloads

## Details

URL | https://github.com/topepo/caret/ |

BugReports | https://github.com/topepo/caret/issues |

License | GPL (>= 2) |

RoxygenNote | 6.0.1 |

NeedsCompilation | yes |

Packaged | 2017-09-01 14:30:16 UTC; max |

Repository | CRAN |

Date/Publication | 2017-09-07 21:15:33 UTC |

suggests | BradleyTerry2 , Cubist , e1071 , earth (>= 2.2-3) , ellipse , fastICA , gam , ipred , kernlab , klaR , MASS , mda , mgcv , mlbench , MLmetrics , nnet , pamr , party (>= 0.9-99992) , pls , pROC , proxy , randomForest , RANN , spls , subselect , superpc , testthat (>= 0.9.1) |

imports | foreach , grDevices , methods , ModelMetrics (>= 1.1.0) , nlme , plyr , recipes (>= 0.0.1) , reshape2 , stats , stats4 , utils , withr (>= 2.0.0) |

depends | ggplot2 , lattice (>= 0.20) , R (>= 2.10) |

Contributors | the Core team, Brenton Kenkel, Max Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tyler Hunt, Tony Cooper, Zachary Mayer, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, Luca Scrucca, Yuan Tang, Can Candan |

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