caret v6.0-82


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Classification and Regression Training

Misc functions for training and plotting classification and regression models.

Functions in caret

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