Functions and data for "Data Mining with R"

Documentation for package ‘DMwR’ version 0.2.0

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-- --

Functions and data for the book "Data Mining with R"

-- A --

algae	Training data for predicting algae blooms
algae.sols	The solutions for the test data set for predicting algae blooms

-- B --

bestScores	Obtain the best scores from an experimental comparison
bootRun	Class "bootRun"
bootRun-class	Class "bootRun"
bootSettings	Class "bootSettings"
bootSettings-class	Class "bootSettings"
bootstrap	Runs a bootstrap experiment

-- C --

centralImputation	Fill in NA values with central statistics
centralValue	Obtain statistic of centrality
compAnalysis	Analyse and print the statistical significance of the differences between a set of learners.
compExp	Class "compExp"
compExp-class	Class "compExp"
CRchart	Plot a Cumulative Recall chart
crossValidation	Run a Cross Validation Experiment
cvRun	Class "cvRun"
cvRun-class	Class "cvRun"
cvSettings	Class "cvSettings"
cvSettings-class	Class "cvSettings"

-- D --

dataset	Class "dataset"
dataset-class	Class "dataset"
dist.to.knn	An auxiliary function of \code{lofactor()}
DMwR	Functions and data for the book "Data Mining with R"
dsNames	Obtain the name of the data sets involved in an experimental comparison

-- E --

experimentalComparison	Carry out Experimental Comparisons Among Learning Systems
expSettings	Class "expSettings"
expSettings-class	Class "expSettings"

-- G --

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.
GSPC	A set of daily quotes for SP500

-- H --

-- J --

join	Merging several \code{compExp} class objects

-- K --

kNN	k-Nearest Neighbour Classification
knneigh.vect	An auxiliary function of \code{lofactor()}
knnImputation	Fill in NA values with the values of the nearest neighbours

-- L --

learner	Class "learner"
learner-class	Class "learner"
learnerNames	Obtain the name of the learning systems involved in an experimental comparison
LinearScaling	Normalize a set of continuous values using a linear scaling
lofactor	An implementation of the LOF algorithm
loocv	Run a Leave One Out Cross Validation Experiment
loocvRun	Class "loocvRun"
loocvRun-class	Class "loocvRun"
loocvSettings	Class "loocvSettings"
loocvSettings-class	Class "loocvSettings"

-- M --

manyNAs	Find rows with too many NA values
mcRun	Class "mcRun"
mcRun-class	Class "mcRun"
mcSettings	Class "mcSettings"
mcSettings-class	Class "mcSettings"
monteCarlo	Run a Monte Carlo experiment

-- O --

Obtain outlier rankings

-- P --

plot-method	Class "compExp"
plot-method	Class "cvRun"
plot-method	Class "hldRun"
plot-method	Class "mcRun"
plot-method	Class "tradeRecord"
PRcurve	Plot a Precision/Recall curve
prettyTree	Visual representation of a tree-based model

-- R --

rankSystems	Provide a ranking of learners involved in an experimental comparison.
reachability	An auxiliary function of \code{lofactor()}
regr.eval	Calculate Some Standard Regression Evaluation Statistics
ReScaling	Re-scales a set of continuous values into a new range using a linear scaling
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

-- S --

sales	A data set with sale transaction reports
SelfTrain	Self train a model on semi-supervised data
show-method	Class "bootSettings"
show-method	Class "compExp"
show-method	Class "cvSettings"
show-method	Class "dataset"
show-method	Class "hldSettings"
show-method	Class "learner"
show-method	Class "loocvSettings"
show-method	Class "mcSettings"
show-method	Class "task"
show-method	Class "tradeRecord"
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.
SMOTE	SMOTE algorithm for unbalanced classification problems
SoftMax	Normalize a set of continuous values using SoftMax
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-method	Methods for Function subset in Package 'DMwR'
subset-methods	Methods for Function subset in Package 'DMwR'
summary-method	Class "bootRun"
summary-method	Class "compExp"
summary-method	Class "cvRun"
summary-method	Class "hldRun"
summary-method	Class "loocvRun"
summary-method	Class "mcRun"
summary-method	Class "tradeRecord"

-- T --

task	Class "task"
task-class	Class "task"
test.algae	Testing data for predicting algae blooms
tradeRecord	Class "tradeRecord"
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

-- U --

Invert the effect of the scale function

-- V --

Generate variants of a learning system