caret v6.0-85


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Classification and Regression Training

Misc functions for training and plotting classification and regression models.

Functions in caret

Name Description
as.matrix.confusionMatrix Confusion matrix as a table
GermanCredit German Credit Data
avNNet Neural Networks Using Model Averaging
pickSizeBest Backwards Feature Selection Helper Functions
caret-internal Internal Functions
createDataPartition Data Splitting functions
densityplot.rfe Lattice functions for plotting resampling results of recursive feature selection
classDist Compute and predict the distances to class centroids
confusionMatrix Create a confusion matrix
Sacramento Sacramento CA Home Prices
findCorrelation Determine highly correlated variables
cars Kelly Blue Book resale data for 2005 model year GM cars
caretSBF Selection By Filtering (SBF) Helper Functions
dhfr Dihydrofolate Reductase Inhibitors Data
dotPlot Create a dotplot of variable importance values
dotplot.diff.resamples Lattice Functions for Visualizing Resampling Differences
featurePlot Wrapper for Lattice Plotting of Predictor Variables
diff.resamples Inferential Assessments About Model Performance
findLinearCombos Determine linear combinations in a matrix
knnreg k-Nearest Neighbour Regression
icr.formula Independent Component Regression
learning_curve_dat Create Data to Plot a Learning Curve
filterVarImp Calculation of filter-based variable importance
histogram.train Lattice functions for plotting resampling results
gafs.default Genetic algorithm feature selection
format.bagEarth Format 'bagEarth' objects
index2vec Convert indicies to a binary vector
cox2 COX-2 Activity Data
confusionMatrix.train Estimate a Resampled Confusion Matrix
knn3 k-Nearest Neighbour Classification
getSamplingInfo Get sampling info from a train model
downSample Down- and Up-Sampling Imbalanced Data
gafs_initial Ancillary genetic algorithm functions
dummyVars Create A Full Set of Dummy Variables
nullModel Fit a simple, non-informative model
mdrr Multidrug Resistance Reversal (MDRR) Agent Data
oneSE Selecting tuning Parameters
panel.lift2 Lattice Panel Functions for Lift Plots
modelLookup Tools for Models Available in train
nearZeroVar Identification of near zero variance predictors
train_model_list A List of Available Models in train
maxDissim Maximum Dissimilarity Sampling
lift Lift Plot
oil Fatty acid composition of commercial oils
plsda Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
plot.gafs Plot Method for the gafs and safs Classes
pcaNNet Neural Networks with a Principal Component Step
panel.needle Needle Plot Lattice Panel
plotClassProbs Plot Predicted Probabilities in Classification Models
ggplot.train Plot Method for the train Class
ggplot.rfe Plot RFE Performance Profiles
defaultSummary Calculates performance across resamples
plotObsVsPred Plot Observed versus Predicted Results in Regression and Classification Models
pottery Pottery from Pre-Classical Sites in Italy
prcomp.resamples Principal Components Analysis of Resampling Results
print.confusionMatrix Print method for confusionMatrix
predictors List predictors used in the model
extractPrediction Extract predictions and class probabilities from train objects
plot.varImp.train Plotting variable importance measures
predict.gafs Predict new samples
predict.knn3 Predictions from k-Nearest Neighbors
resampleHist Plot the resampling distribution of the model statistics
predict.knnreg Predictions from k-Nearest Neighbors Regression Model
resampleSummary Summary of resampled performance estimates
rfe Backwards Feature Selection
resamples Collation and Visualization of Resampling Results
gafsControl Control parameters for GA and SA feature selection
sbf Selection By Filtering (SBF)
spatialSign Compute the multivariate spatial sign
negPredValue Calculate sensitivity, specificity and predictive values
rfeControl Controlling the Feature Selection Algorithms
scat Morphometric Data on Scat
segmentationData Cell Body Segmentation
sbfControl Control Object for Selection By Filtering (SBF)
safs Simulated annealing feature selection
predict.bagEarth Predicted values based on bagged Earth and FDA models
preProcess Pre-Processing of Predictors
update.train Update or Re-fit a Model
update.safs Update or Re-fit a SA or GA Model
print.train Print Method for the train Class
varImp Calculation of variable importance for regression and classification models
recall Calculate recall, precision and F values
trainControl Control parameters for train
safs_initial Ancillary simulated annealing functions
summary.bagEarth Summarize a bagged earth or FDA fit
tecator Fat, Water and Protein Content of Meat Samples
train Fit Predictive Models over Different Tuning Parameters
SLC14_1 Simulation Functions
var_seq Sequences of Variables for Tuning
xyplot.resamples Lattice Functions for Visualizing Resampling Results
thresholder Generate Data to Choose a Probability Threshold
varImp.gafs Variable importances for GAs and SAs
bag A General Framework For Bagging
BloodBrain Blood Brain Barrier Data
BoxCoxTrans Box-Cox and Exponential Transformations
bagFDA Bagged FDA
calibration Probability Calibration Plot
bagEarth Bagged Earth
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