# 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!