# 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 | |

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 | |

No Results! |

## Last month downloads

## Details

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 |

imports | abind (>= 1.1-0) , class (>= 7.3-1) , quantmod (>= 0.3-8) , ROCR (>= 1.0) , rpart (>= 3.1-46) , xts (>= 0.6-7) , zoo (>= 1.6-4) |

depends | base (>= 2.10) , graphics , grid (>= 2.10.1) , lattice (>= 0.18-3) , methods , R (>= 2.10) |

Contributors | Luis Torgo |

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