FAQs: CMML or Predictive Coding Workflow?

Q: Will predictive coding be used to cull a dataset prior to a later review phase, or is the goal to find the documents of interest during iterative training and review?

A: Either type of workflow may be used for culling. CMML is ideal for workflows where the goal is to find documents of interest as quickly as possible.  

Q: Are statistical estimates of the recall, precision, and other measures of effectiveness of the predictive model needed as part of the reporting on the predictive coding process?

A: If so, a PC workflow is more appropriate.

Q: Is a statistical estimate needed of how many documents of interest were not found during training?

A: If so, a CMML workflow may be more appropriate.

Q: Is it desired to use all review documents for training?

A: A CMML workflow does not distinguish between review and training, and so is most natural when this is desired.

Q: Is the total number of target documents, and/or the proportion of target documents, very low?

A: If so, using a Control Set may be impractical. While a dummy Control Set may be used with a PC workflow, a CMML workflow is often preferable in this case.

Q: Is an iterative relevance feedback (training on top-ranked documents) approach to training desired?

A: This can be done in either workflow, but is easier in a CMML workflow.

Q: Is it desired to simultaneously train predictive models for several different topics?

A: This is only supported for CMML workflows.

Q: Is the topic of interest expected to evolve as documents are examined during training?

A: A CMML workflow makes it easier to change the coding of documents, remove documents from training, and flexibly pursue an evolving information need.

Q: Is it desired to a use a predictive model in combination with other analytics tools (for instance, to view top score documents on the cluster wheel)?

A: This is much simpler in a CMML workflow.