Man pages for DMwR
Functions and data for "Data Mining with R"
| algae | Training data for predicting algae blooms |
| algae.sols | The solutions for the test data set for predicting algae... |
| bestScores | Obtain the best scores from an experimental comparison |
| bootRun-class | Class "bootRun" |
| bootSettings-class | Class "bootSettings" |
| bootstrap | Runs a bootstrap experiment |
| 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... |
| compExp-class | Class "compExp" |
| CRchart | Plot a Cumulative Recall chart |
| crossValidation | Run a Cross Validation Experiment |
| cvRun-class | Class "cvRun" |
| cvSettings-class | Class "cvSettings" |
| dataset-class | Class "dataset" |
| dist.to.knn | An auxiliary function of 'lofactor()' |
| DMwR-defunct | Defunct Functions in Package 'DMwR' |
| DMwR-package | Functions and data for the book "Data Mining with R" |
| dsNames | Obtain the name of the data sets involved in an experimental... |
| experimentalComparison | Carry out Experimental Comparisons Among Learning Systems |
| expSettings-class | Class "expSettings" |
| getFoldsResults | Obtain the results on each iteration of a learner |
| getSummaryResults | Obtain a set of descriptive statistics of the results of a... |
| getVariant | Obtain the learner associated with an identifier within a... |
| growingWindowTest | Obtain the predictions of a model using a growing window... |
| GSPC | A set of daily quotes for SP500 |
| hldRun-class | Class "hldRun" |
| hldSettings-class | Class "hldSettings" |
| holdOut | Runs a Hold Out experiment |
| join | Merging several 'compExp' class objects |
| kNN | k-Nearest Neighbour Classification |
| knneigh.vect | An auxiliary function of 'lofactor()' |
| knnImputation | Fill in NA values with the values of the nearest neighbours |
| learner-class | Class "learner" |
| learnerNames | Obtain the name of the learning systems involved in an... |
| 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 | Class "loocvRun" |
| loocvSettings-class | Class "loocvSettings" |
| manyNAs | Find rows with too many NA values |
| mcRun-class | Class "mcRun" |
| mcSettings-class | Class "mcSettings" |
| monteCarlo | Run a Monte Carlo experiment |
| outliers.ranking | Obtain outlier rankings |
| PRcurve | Plot a Precision/Recall curve |
| prettyTree | Visual representation of a tree-based model |
| rankSystems | Provide a ranking of learners involved in an experimental... |
| 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... |
| 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 |
| sales | A data set with sale transaction reports |
| SelfTrain | Self train a model on semi-supervised data |
| sigs.PR | Precision and recall of a set of predicted trading signals |
| slidingWindowTest | Obtain the predictions of a model using a sliding window... |
| 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... |
| statScores | Obtains a summary statistic of one of the evaluation metrics... |
| subset-methods | Methods for Function subset in Package 'DMwR' |
| task-class | Class "task" |
| test.algae | Testing data for predicting algae blooms |
| tradeRecord-class | Class "tradeRecord" |
| tradingEvaluation | Obtain a set of evaluation metrics for a set of trading... |
| trading.signals | Discretize a set of values into a set of trading signals |
| trading.simulator | Simulate daily trading using a set of trading signals |
| ts.eval | Calculate Some Standard Evaluation Statistics for Time Series... |
| unscale | Invert the effect of the scale function |
| variants | Generate variants of a learning system |
