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caret (version 6.0-34)
Classification and Regression Training
Description
Misc functions for training and plotting classification and regression models
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Install
install.packages('caret')
Monthly Downloads
160,352
Version
6.0-34
License
GPL-2
Maintainer
Max Kuhn
Last Published
August 15th, 2014
Functions in caret (6.0-34)
Search functions
avNNet.default
Neural Networks Using Model Averaging
confusionMatrix.train
Estimate a Resampled Confusion Matrix
knn3
k-Nearest Neighbour Classification
diff.resamples
Inferential Assessments About Model Performance
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
plot.train
Plot Method for the train Class
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
plot.varImp.train
Plotting variable importance measures
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
findLinearCombos
Determine linear combinations in a matrix
lift
Lift Plot
cars
Kelly Blue Book resale data for 2005 model year GM cars
print.train
Print Method for the train Class
bag.default
A General Framework For Bagging
sbf
Selection By Filtering (SBF)
update.train
Update or Re-fit a Model
format.bagEarth
Format 'bagEarth' objects
as.table.confusionMatrix
Save Confusion Table Results
createDataPartition
Data Splitting functions
knnreg
k-Nearest Neighbour Regression
preProcess
Pre-Processing of Predictors
plot.rfe
Plot RFE Performance Profiles
calibration
Probability Calibration Plot
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
nullModel
Fit a simple, non-informative model
nearZeroVar
Identification of near zero variance predictors
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
bagFDA
Bagged FDA
classDist
Compute and predict the distances to class centroids
rfeControl
Controlling the Feature Selection Algorithms
filterVarImp
Calculation of filter-based variable importance
resampleSummary
Summary of resampled performance estimates
postResample
Calculates performance across resamples
resampleHist
Plot the resampling distribution of the model statistics
summary.bagEarth
Summarize a bagged earth or FDA fit
sbfControl
Control Object for Selection By Filtering (SBF)
train_model_list
A List of Available Models in train
predict.train
Extract predictions and class probabilities from train objects
findCorrelation
Determine highly correlated variables
print.confusionMatrix
Print method for confusionMatrix
caretSBF
Selection By Filtering (SBF) Helper Functions
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
trainControl
Control parameters for train
oneSE
Selecting tuning Parameters
BoxCoxTrans.default
Box-Cox and Exponential Transformations
histogram.train
Lattice functions for plotting resampling results
confusionMatrix
Create a confusion matrix
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
downSample
Down- and Up-Sampling Imbalanced Data
dhfr
Dihydrofolate Reductase Inhibitors Data
resamples
Collation and Visualization of Resampling Results
BloodBrain
Blood Brain Barrier Data
predict.bagEarth
Predicted values based on bagged Earth and FDA models
GermanCredit
German Credit Data
spatialSign
Compute the multivariate spatial sign
segmentationData
Cell Body Segmentation
varImp
Calculation of variable importance for regression and classification models
dotPlot
Create a dotplot of variable importance values
sensitivity
Calculate sensitivity, specificity and predictive values
modelLookup
Tools for Models Available in
train
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
panel.lift2
Lattice Panel Functions for Lift Plots
dummyVars
Create A Full Set of Dummy Variables
maxDissim
Maximum Dissimilarity Sampling
plotClassProbs
Plot Predicted Probabilities in Classification Models
prcomp.resamples
Principal Components Analysis of Resampling Results
oil
Fatty acid composition of commercial oils
pottery
Pottery from Pre-Classical Sites in Italy
caretFuncs
Backwards Feature Selection Helper Functions
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
tecator
Fat, Water and Protein Content of Meat Samples
pcaNNet.default
Neural Networks with a Principal Component Step
rfe
Backwards Feature Selection
bagEarth
Bagged Earth
twoClassSim
Simulation Functions
predict.knn3
Predictions from k-Nearest Neighbors
icr.formula
Independent Component Regression
predictors
List predictors used in the model
caret-internal
Internal Functions
panel.needle
Needle Plot Lattice Panel
cox2
COX-2 Activity Data
train
Fit Predictive Models over Different Tuning Parameters