Man pages for DMwR2
Functions and Data for the Second Edition of "Data Mining with R"
| algae | Training data for predicting algae blooms |
| algae.sols | The solutions for the test data set for predicting algae... |
| centralImputation | Fill in NA values with central statistics |
| centralValue | Obtain statistic of centrality |
| createEmbedDS | Creates an embeded data set from an univariate time series |
| dist.to.knn | An auxiliary function of 'lofactor()' |
| DMwR2-package | Functions and data for the second edition of the book "Data... |
| GSPC | A set of daily quotes for SP500 |
| kNN | k-Nearest Neighbour Classification |
| knneigh.vect | An auxiliary function of 'lofactor()' |
| knnImputation | Fill in NA values with the values of the nearest neighbours |
| lofactor | An implementation of the LOF algorithm |
| manyNAs | Find rows with too many NA values |
| nrLinesFile | Counts the number of lines of a file |
| outliers.ranking | Obtain outlier rankings |
| reachability | An auxiliary function of 'lofactor()' |
| rpartXse | Obtain a tree-based model |
| rt.prune | Prune a tree-based model using the SE rule |
| sales | A data set with sale transaction reports |
| sampleCSV | Drawing a random sample of lines from a CSV file |
| sampleDBMS | Drawing a random sample of records of a table stored in a... |
| SelfTrain | Self train a model on semi-supervised data |
| sigs.PR | Precision and recall of a set of predicted trading signals |
| SoftMax | Normalize a set of continuous values using SoftMax |
| sp500 | A set of daily quotes for SP500 in CSV Format |
| 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 |
