R: Generate Training Set

genSamples {RecordLinkage} | R Documentation

## Generate Training Set

### Description

Generates training data by unsupervised classification.

### Usage

    genSamples(dataset, num.non, des.mprop = 0.1)

### Arguments

`dataset` | Object of class `RecLinkData`. Data pairs from which to sample.
`num.non` | Positive Integer. Number of desired non-links in the training set.
`des.mprop` | Real number in the range [0,1]. Ratio of number of links to number of non-links in the training set.

### Details

The application of supervised classifiers (via `classifySupv`) requires a training set of record pairs with known matching status. Where no such data are available, `genSamples` can be used to generate training data. The matching status is determined by unsupervised clustering with `bclust`. Subsequently, the desired number of links and non-links are sampled.

If the requested numbers of matches or non-matches is not feasible, a warning is issued and the maximum possible number is considered.

### Value

A list of `"RecLinkResult"` objects.

`train` | The sampled training data.
`valid` | All other record pairs

Record pairs are split into the respective `pairs` components. The `prediction` components represent the clustering result. If weights are present in `dataset`, the corresponding values of `Wdata` are stored to `train` and `valid`. All other components are copied from `dataset`.

### Note

Unsupervised clustering may lead to a poor quality of classification, all subsequent results should be evaluated critically.

### Author(s)

Andreas Borg, Murat Sariyar

### See Also

`splitData` for splitting data sets without clustering.

[Package RecordLinkage version 0.4-11.2 Index]