Functions and data for "Data Mining with R"

Documentation for package ‘DMwR’ version 0.4.1

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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
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 "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 '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 'compExp' class objects

-- K --

kNN	k-Nearest Neighbour Classification
knneigh.vect	An auxiliary function of '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 '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
ts.eval	Calculate Some Standard Evaluation Statistics for Time Series Forecasting Tasks

-- U --

Invert the effect of the scale function

-- V --

Generate variants of a learning system