This is used only with RLINK queries.

When TIBCO Patterns Learn Model calculates a score it can also calculate a measure of how confident it is that the score generated is reliable. The reliability of the score depends on how well trained the model is on similar pairs of records.

There are a number of different confidence measures that can be used. The measures available depend on the release version of the model. Confidence measures can not be calculated for models versions less than RFV3. The feature confidence measure is available only on model versions RFV6 or higher (corresponding to TIBCO Patterns - Search release 5.4, but note it is the model version, not the TIBCO Patterns - Search version, that matters, a release 5.4 TIBCO Patterns - Search server using a model generated by a previous release does not support feature confidence measures). The feature confidence measure produces the most reliable measure of how well trained the model is for a particular pair of records. However it is also by far the most expensive to compute. Therefore it is not the default measure used.

Generally the feature confidence measure is used in applications that are specifically looking for poorly trained pairs or records. These may be in applications implementing a continuous learning capability, or model training platforms. It could also be used in trouble shooting applications to help determine why a model came up with a questionable prediction.

Calling this method will cause the feature based confidence measure to be used rather than the default measure.