## Classification and Regression Training

Misc functions for training and plotting classification and
regression models.

## Functions in caret

Name | Description | |

bag | A General Framework For Bagging | |

pickSizeBest | Backwards Feature Selection Helper Functions | |

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

bagFDA | Bagged FDA | |

BoxCoxTrans | Box-Cox and Exponential Transformations | |

calibration | Probability Calibration Plot | |

bagEarth | Bagged Earth | |

caret-internal | Internal Functions | |

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

confusionMatrix.train | Estimate a Resampled Confusion Matrix | |

featurePlot | Wrapper for Lattice Plotting of Predictor Variables | |

createDataPartition | Data Splitting functions | |

dotPlot | Create a dotplot of variable importance values | |

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

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

dummyVars | Create A Full Set of Dummy Variables | |

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

diff.resamples | Inferential Assessments About Model Performance | |

GermanCredit | German Credit Data | |

findLinearCombos | Determine linear combinations in a matrix | |

gafs_initial | Ancillary genetic algorithm functions | |

getSamplingInfo | Get sampling info from a train model | |

gafs.default | Genetic algorithm feature selection | |

format.bagEarth | Format 'bagEarth' objects | |

filterVarImp | Calculation of filter-based variable importance | |

findCorrelation | Determine highly correlated variables | |

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

icr.formula | Independent Component Regression | |

lift | Lift Plot | |

maxDissim | Maximum Dissimilarity Sampling | |

learing_curve_dat | Create Data to Plot a Learning Curve | |

knn3 | k-Nearest Neighbour Classification | |

modelLookup | Tools for Models Available in train | |

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

nearZeroVar | Identification of near zero variance predictors | |

index2vec | Convert indicies to a binary vector | |

knnreg | k-Nearest Neighbour Regression | |

pcaNNet | Neural Networks with a Principal Component Step | |

train_model_list | A List of Available Models in train | |

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

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

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

panel.needle | Needle Plot Lattice Panel | |

plotClassProbs | Plot Predicted Probabilities in Classification Models | |

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

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

ggplot.rfe | Plot RFE Performance Profiles | |

preProcess | Pre-Processing of Predictors | |

oneSE | Selecting tuning Parameters | |

print.confusionMatrix | Print method for confusionMatrix | |

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

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

predictors | List predictors used in the model | |

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

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

predict.gafs | Predict new samples | |

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

resamples | Collation and Visualization of Resampling Results | |

resampleSummary | Summary of resampled performance estimates | |

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

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

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

avNNet | Neural Networks Using Model Averaging | |

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

var_seq | Sequences of Variables for Tuning | |

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

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

sbf | Selection By Filtering (SBF) | |

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

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

trainControl | Control parameters for train | |

rfeControl | Controlling the Feature Selection Algorithms | |

SLC14_1 | Simulation Functions | |

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

rfe | Backwards Feature Selection | |

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

spatialSign | Compute the multivariate spatial sign | |

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

safs_initial | Ancillary simulated annealing functions | |

safs | Simulated annealing feature selection | |

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

train | Fit Predictive Models over Different Tuning Parameters | |

No Results! |

## Last month downloads

## Details

Date | 2016-11-08 |

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

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

License | GPL (>= 2) |

RoxygenNote | 5.0.1 |

NeedsCompilation | yes |

Packaged | 2016-11-09 08:31:15 UTC; kuhna03 |

Repository | CRAN |

Date/Publication | 2016-11-10 08:18:44 |

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 | car , foreach , grDevices , methods , ModelMetrics (>= 1.1.0) , nlme , plyr , reshape2 , stats , stats4 , utils |

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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