classifySupv: Supervised Classification

In RecordLinkage: Record Linkage Functions for Linking and Deduplicating Data Sets

Supervised Classification

Description

Supervised classification of record pairs based on a trained model.

Usage

  classifySupv(model, newdata, ...)

  ## S4 method for signature 'RecLinkClassif,RecLinkData'
classifySupv(model, newdata,
    convert.na = TRUE, ...)

  ## S4 method for signature 'RecLinkClassif,RLBigData'
classifySupv(model, newdata,
    convert.na = TRUE, withProgressBar = (sink.number()==0), ...)

Arguments

model	Object of class RecLinkClassif. The calibrated model. See trainSupv.
newdata	Object of class "RecLinkData" or "RLBigData". The data to classify.
convert.na	Logical. Whether to convert missing values in the comparison patterns to 0.
withProgressBar	Whether to display a progress bar
...	Further arguments for the predict method.

Details

The record pairs in newdata are classified by calling the appropriate predict method for model$model.

By default, the "RLBigDataDedup" method displays a progress bar unless output is diverted by sink, e.g. when processing a Sweave file.

Value

For the "RecLinkData" method, a S3 object of class "RecLinkResult" that represents a copy of newdata with element rpairs$prediction, which stores the classification result, as addendum.

For the "RLBigData" method, a S4 object of class "RLResult".

Author(s)

Andreas Borg, Murat Sariyar

See Also

trainSupv for training of classifiers, classifyUnsup for unsupervised classification.

Examples

# Split data into training and validation set, train and classify with rpart
data(RLdata500)
pairs=compare.dedup(RLdata500, identity=identity.RLdata500,
                    blockfld=list(1,3,5,6,7))
l=splitData(pairs, prop=0.5, keep.mprop=TRUE)                    
model=trainSupv(l$train, method="rpart", minsplit=5)
result=classifySupv(model=model, newdata=l$valid)
summary(result)

RecordLinkage documentation built on Nov. 10, 2022, 5:42 p.m.