CRchart                 Plot a Cumulative Recall chart
DMwR-package            Functions and data for the book "Data Mining
                        with R"
GSPC                    A set of daily quotes for SP500
LinearScaling           Normalize a set of continuous values using a
                        linear scaling
PRcurve                 Plot a Precision/Recall curve
ReScaling               Re-scales a set of continuous values into a new
                        range using a linear scaling
SMOTE                   SMOTE algorithm for unbalanced classification
                        problems
SelfTrain               Self train a model on semi-supervised data
SoftMax                 Normalize a set of continuous values using
                        SoftMax
algae                   Training data for predicting algae blooms
algae.sols              The solutions for the test data set for
                        predicting algae blooms
bestScores              Obtain the best scores from an experimental
                        comparison
bootRun-class           Class "bootRun"
bootSettings-class      Class "bootSettings"
bootstrap               Runs a bootstrap experiment
centralImputation       Fill in NA values with central statistics
centralValue            Obtain statistic of centrality
class.eval              Calculate Some Standard Classification
                        Evaluation Statistics
compAnalysis            Analyse and print the statistical significance
                        of the differences between a set of learners.
compExp-class           Class "compExp"
crossValidation         Run a Cross Validation Experiment
cvRun-class             Class "cvRun"
cvSettings-class        Class "cvSettings"
dataset-class           Class "dataset"
dist.to.knn             An auxiliary function of 'lofactor()'
dsNames                 Obtain the name of the data sets involved in an
                        experimental comparison
expSettings-class       Class "expSettings"
experimentalComparison
                        Carry out Experimental Comparisons Among
                        Learning Systems
getFoldsResults         Obtain the results on each iteration of a
                        learner
getSummaryResults       Obtain a set of descriptive statistics of the
                        results of a learner
getVariant              Obtain the learner associated with an
                        identifier within a comparison
growingWindowTest       Obtain the predictions of a model using a
                        growing window learning approach.
hldRun-class            Class "hldRun"
hldSettings-class       Class "hldSettings"
holdOut                 Runs a Hold Out experiment
join                    Merging several 'compExp' class objects
kNN                     k-Nearest Neighbour Classification
knnImputation           Fill in NA values with the values of the
                        nearest neighbours
knneigh.vect            An auxiliary function of 'lofactor()'
learner-class           Class "learner"
learnerNames            Obtain the name of the learning systems
                        involved in an experimental comparison
lofactor                An implementation of the LOF algorithm
loocv                   Run a Leave One Out Cross Validation Experiment
loocvRun-class          Class "loocvRun"
loocvSettings-class     Class "loocvSettings"
manyNAs                 Find rows with too many NA values
mcRun-class             Class "mcRun"
mcSettings-class        Class "mcSettings"
monteCarlo              Run a Monte Carlo experiment
outliers.ranking        Obtain outlier rankings
prettyTree              Visual representation of a tree-based model
rankSystems             Provide a ranking of learners involved in an
                        experimental comparison.
reachability            An auxiliary function of 'lofactor()'
regr.eval               Calculate Some Standard Regression Evaluation
                        Statistics
resp                    Obtain the target variable values of a
                        prediction problem
rpartXse                Obtain a tree-based model
rt.prune                Prune a tree-based model using the SE rule
runLearner              Run a Learning Algorithm
sales                   A data set with sale transaction reports
sigs.PR                 Precision and recall of a set of predicted
                        trading signals
slidingWindowTest       Obtain the predictions of a model using a
                        sliding window learning approach.
statNames               Obtain the name of the statistics involved in
                        an experimental comparison
statScores              Obtains a summary statistic of one of the
                        evaluation metrics used in an experimental
                        comparison, for all learners and data sets
                        involved in the comparison.
subset-methods          Methods for Function subset in Package 'DMwR'
task-class              Class "task"
test.algae              Testing data for predicting algae blooms
tradeRecord-class       Class "tradeRecord"
trading.signals         Discretize a set of values into a set of
                        trading signals
trading.simulator       Simulate daily trading using a set of trading
                        signals
tradingEvaluation       Obtain a set of evaluation metrics for a set of
                        trading actions
ts.eval                 Calculate Some Standard Evaluation Statistics
                        for Time Series Forecasting Tasks
unscale                 Invert the effect of the scale function
variants                Generate variants of a learning system
