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caret (version 6.0-82)

Classification and Regression Training

Description

Misc functions for training and plotting classification and regression models.

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install.packages('caret')

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6.0-82

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GPL (>= 2)

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Last Published

March 26th, 2019

Functions in caret (6.0-82)

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