# caret v6.0-86

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## Classification and Regression Training

Misc functions for training and plotting classification and
regression models.

## Functions in caret

Name | Description | |

avNNet | Neural Networks Using Model Averaging | |

Sacramento | Sacramento CA Home Prices | |

BloodBrain | Blood Brain Barrier Data | |

bagFDA | Bagged FDA | |

GermanCredit | German Credit Data | |

bag | A General Framework For Bagging | |

calibration | Probability Calibration Plot | |

createDataPartition | Data Splitting functions | |

bagEarth | Bagged Earth | |

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

BoxCoxTrans | Box-Cox and Exponential Transformations | |

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

caret-internal | Internal Functions | |

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

confusionMatrix.train | Estimate a Resampled Confusion Matrix | |

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

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

dotPlot | Create a dotplot of variable importance values | |

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

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

confusionMatrix | Create a confusion matrix | |

cox2 | COX-2 Activity Data | |

format.bagEarth | Format 'bagEarth' objects | |

featurePlot | Wrapper for Lattice Plotting of Predictor Variables | |

diff.resamples | Inferential Assessments About Model Performance | |

dummyVars | Create A Full Set of Dummy Variables | |

dhfr | Dihydrofolate Reductase Inhibitors Data | |

filterVarImp | Calculation of filter-based variable importance | |

index2vec | Convert indicies to a binary vector | |

gafs_initial | Ancillary genetic algorithm functions | |

pickSizeBest | Backwards Feature Selection Helper Functions | |

getSamplingInfo | Get sampling info from a train model | |

findLinearCombos | Determine linear combinations in a matrix | |

gafs.default | Genetic algorithm feature selection | |

knn3 | k-Nearest Neighbour Classification | |

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

knnreg | k-Nearest Neighbour Regression | |

oneSE | Selecting tuning Parameters | |

icr.formula | Independent Component Regression | |

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

train_model_list | A List of Available Models in train | |

lift | Lift Plot | |

modelLookup | Tools for Models Available in train | |

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

findCorrelation | Determine highly correlated variables | |

maxDissim | Maximum Dissimilarity Sampling | |

nearZeroVar | Identification of near zero variance predictors | |

learning_curve_dat | Create Data to Plot a Learning Curve | |

plotClassProbs | Plot Predicted Probabilities in Classification Models | |

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

panel.needle | Needle Plot Lattice Panel | |

ggplot.rfe | Plot RFE Performance Profiles | |

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

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

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

oil | Fatty acid composition of commercial oils | |

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

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

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

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

preProcess | Pre-Processing of Predictors | |

safs_initial | Ancillary simulated annealing functions | |

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

pcaNNet | Neural Networks with a Principal Component Step | |

rfe | Backwards Feature Selection | |

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

rfeControl | Controlling the Feature Selection Algorithms | |

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

defaultSummary | Calculates performance across resamples | |

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

predict.gafs | Predict new samples | |

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

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

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

resamples | Collation and Visualization of Resampling Results | |

resampleSummary | Summary of resampled performance estimates | |

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

recall | Calculate recall, precision and F values | |

safs | Simulated annealing feature selection | |

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

sbf | Selection By Filtering (SBF) | |

predictors | List predictors used in the model | |

print.confusionMatrix | Print method for confusionMatrix | |

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

segmentationData | Cell Body Segmentation | |

thresholder | Generate Data to Choose a Probability Threshold | |

var_seq | Sequences of Variables for Tuning | |

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

scat | Morphometric Data on Scat | |

trainControl | Control parameters for train | |

negPredValue | Calculate sensitivity, specificity and predictive values | |

SLC14_1 | Simulation Functions | |

spatialSign | Compute the multivariate spatial sign | |

train | Fit Predictive Models over Different Tuning Parameters | |

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

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

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

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

No Results! |

## Vignettes of caret

Name | ||

caret.Rmd | ||

train_algo.png | ||

No Results! |

## Last month downloads

## Details

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

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

License | GPL (>= 2) |

RoxygenNote | 7.0.2.9000 |

VignetteBuilder | knitr |

Encoding | UTF-8 |

NeedsCompilation | yes |

Packaged | 2020-03-20 03:09:16 UTC; max |

Repository | CRAN |

Date/Publication | 2020-03-20 10:20:07 UTC |

imports | , foreach , grDevices , methods , ModelMetrics (>= 1.2.2.2) , nlme , plyr , pROC , recipes (>= 0.1.10) , reshape2 , stats , stats4 , utils , withr (>= 2.0.0) |

suggests | BradleyTerry2 , covr , Cubist , dplyr , e1071 , earth (>= 2.2-3) , ellipse , fastICA , gam (>= 1.15) , ipred , kernlab , klaR , knitr , MASS , mda , mgcv , mlbench , MLmetrics , nnet , pamr , party (>= 0.9-99992) , pls , proxy , randomForest , RANN , rpart , spls , subselect , superpc , testthat (>= 0.9.1) |

depends | ggplot2 , lattice (>= 0.20) , R (>= 3.2.0) |

Contributors | R Core team, Brenton Kenkel, 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, Jed Wing |

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