EDIT: Hello readers, these articles are now 4 years old and many of the Watson services and APIs have moved or been changed. The concepts discussed in these articles are still relevant but I am working on 2nd editions of them.
Last time we discussed some good practices for collecting data and then splitting it into test and train in order to create a ground truth for your machine learning system. We then talked about calculating accuracy using test and blind data sets.
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James Ravenscroft
EDIT: Hello readers, these articles are now 4 years old and many of the Watson services and APIs have moved or been changed. The concepts discussed in these articles are still relevant but I am working on 2nd editions of them.
Quality assurance is arguably one of the most important parts of the software development lifecycle. In order to release a product that is production ready, it must be put under, and pass, a number of tests – these include unit testing, boundary testing, stress testing and other practices that many software testers are no doubt familiar with. The ways in which traditional software are relatively clear.In a normal system, developers write deterministic functions, that is – if you put an input parameter in, unless there is a bug, you will always get the same output back. This principal makes it.. well not easy… but less difficult to write good test scripts and know that there is a bug or regression in your system if these scripts get a different answer back than usual.