# Functions and data for "Data Mining with R"

## Documentation for package ‘DMwR’ version 0.4.1

- DESCRIPTION file.

## Help Pages

A B C D E G H J K L M O P R S T U V

DMwR-package | 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 --

hldRun | Class "hldRun"
hldRun-class | Class "hldRun"
hldSettings | Class "hldSettings"
hldSettings-class | Class "hldSettings"
holdOut | Runs a Hold Out experiment

## -- 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 --

outliers.ranking | 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 --

unscale | Invert the effect of the scale function

## -- V --

variants | Generate variants of a learning system