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Author here. One of the reasons to start with something even simpler than embeddings such as fastText/Word2Vec, is their ease of explainability. As this blog po
by e_ameisen 9y ago
Author here. One of the reasons to start with something even simpler than embeddings such as fastText/Word2Vec, is their ease of explainability. As this blog post alludes to, the majority of ML work is data work, and in order to efficiently work on your data, understanding what your model does right/wrong is critical. This is why we favor starting with approaches that might seem simple or outdated. Once you see how these approaches fail, you can better inform your choice of a complex model.
- KennyCason 9y agoHaving worked on modeling data and sharing results with customers, I agree simpler solutions can be better for ease of explaining alone. Additionally, the loss of x percent accuracy with lower-tech solutions can sometimes be worth it because they are easier to train and reason with. This is particularly the case when you are just looking for loose directional indicators and correlations. If simple solution gets 77% accuracy, and complex solution yields 80%, I would wager that most of the time you should just stick with the simple solution. One specific example that comes to mind is in sentiment analysis where you can achieve "sufficiently" high accuracy with well tailored Bayesian approaches. They are super fast to train and reason with. If a customer/consumer wants to know exactly why a piece of text is positive or negative, the n-gram probability matrix is extremely easy to inspect. Subsequent re-training and fixing is also much easier than re-training a large neural network, svm, etc.
- kk58 9y agoCan you explain how you reduced sentences to 2 dimensional entities using PCA ?