DMwR v0.4.1


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Functions and data for "Data Mining with R"

This package includes functions and data accompanying the book "Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.

Functions in DMwR

Name Description
knnImputation Fill in NA values with the values of the nearest neighbours
SelfTrain Self train a model on semi-supervised data
experimentalComparison Carry out Experimental Comparisons Among Learning Systems
resp Obtain the target variable values of a prediction problem
getFoldsResults Obtain the results on each iteration of a learner
getVariant Obtain the learner associated with an identifier within a comparison
loocvRun-class Class "loocvRun"
crossValidation Run a Cross Validation Experiment
LinearScaling Normalize a set of continuous values using a linear scaling
growingWindowTest Obtain the predictions of a model using a growing window learning approach.
algae Training data for predicting algae blooms
cvSettings-class Class "cvSettings"
runLearner Run a Learning Algorithm
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.
centralImputation Fill in NA values with central statistics
reachability An auxiliary function of lofactor()
SoftMax Normalize a set of continuous values using SoftMax
knneigh.vect An auxiliary function of lofactor()
task-class Class "task"
hldSettings-class Class "hldSettings"
trading.simulator Simulate daily trading using a set of trading signals
getSummaryResults Obtain a set of descriptive statistics of the results of a learner
DMwR-defunct Defunct Functions in Package DMwR
mcRun-class Class "mcRun"
bestScores Obtain the best scores from an experimental comparison
PRcurve Plot a Precision/Recall curve
cvRun-class Class "cvRun"
dsNames Obtain the name of the data sets involved in an experimental comparison
ts.eval Calculate Some Standard Evaluation Statistics for Time Series Forecasting Tasks
rt.prune Prune a tree-based model using the SE rule
GSPC A set of daily quotes for SP500
bootstrap Runs a bootstrap experiment
slidingWindowTest Obtain the predictions of a model using a sliding window learning approach.
bootSettings-class Class "bootSettings"
mcSettings-class Class "mcSettings"
class.eval Calculate Some Standard Classification Evaluation Statistics
rankSystems Provide a ranking of learners involved in an experimental comparison.
prettyTree Visual representation of a tree-based model
test.algae Testing data for predicting algae blooms
centralValue Obtain statistic of centrality
loocvSettings-class Class "loocvSettings"
rpartXse Obtain a tree-based model
DMwR-package Functions and data for the book "Data Mining with R"
algae.sols The solutions for the test data set for predicting algae blooms
learnerNames Obtain the name of the learning systems involved in an experimental comparison
kNN k-Nearest Neighbour Classification An auxiliary function of lofactor()
ReScaling Re-scales a set of continuous values into a new range using a linear scaling
sales A data set with sale transaction reports
holdOut Runs a Hold Out experiment
tradingEvaluation Obtain a set of evaluation metrics for a set of trading actions
bootRun-class Class "bootRun"
learner-class Class "learner"
dataset-class Class "dataset"
expSettings-class Class "expSettings"
join Merging several compExp class objects
hldRun-class Class "hldRun"
manyNAs Find rows with too many NA values
lofactor An implementation of the LOF algorithm
outliers.ranking Obtain outlier rankings
monteCarlo Run a Monte Carlo experiment
tradeRecord-class Class "tradeRecord"
trading.signals Discretize a set of values into a set of trading signals
loocv Run a Leave One Out Cross Validation Experiment
subset-methods Methods for Function subset in Package `DMwR'
CRchart Plot a Cumulative Recall chart
sigs.PR Precision and recall of a set of predicted trading signals
unscale Invert the effect of the scale function
SMOTE SMOTE algorithm for unbalanced classification problems
compExp-class Class "compExp"
compAnalysis Analyse and print the statistical significance of the differences between a set of learners.
regr.eval Calculate Some Standard Regression Evaluation Statistics
variants Generate variants of a learning system
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Type Package
Date 2013-08-08
License GPL (>= 2)
LazyLoad yes
LazyData yes
Packaged 2013-08-08 15:59:14 UTC; ltorgo
NeedsCompilation no
Repository CRAN
Date/Publication 2013-08-08 19:46:37

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