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DMwR (version 0.4.1)
Functions and data for "Data Mining with R"
Description
This package includes functions and data accompanying the book "Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
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Install
install.packages('DMwR')
Monthly Downloads
191
Version
0.4.1
License
GPL (>= 2)
Maintainer
Luis Torgo
Last Published
August 8th, 2013
Functions in DMwR (0.4.1)
Search functions
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
dist.to.knn
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