Precision, Recall, and F1-Score. These relate to getting a finer-grained idea of how well a classifier is doing, as opposed to just looking at overall accuracy. In this article, I provide a bit deeper look at each.
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aditional software. With traditional software, the specification and its relation to the implementation is typically quite explicit. With more complex machine learning-based system, this relation is harder to explicitly define. This makes testing them more complicated. In this article, I present an updated version of my earlier work on using metamorphic testing for ML based systems.
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results these days. Libraries such as Keras, PyTorch, and HuggingFace NLP make the application of the latest research and models in the area a (relatively) easy task. In this article, I implement and compare two different NLP based classifier model architectures using the Firefox browser issue tracker data.
Read More Building an NLP classifier: Example with Firefox Issue Reports