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What you use now is a simple KNN classifier, and if it works well enough, perhaps no need to go much further. If you need to squeeze out a couple additional per
by pama 2y ago
What you use now is a simple KNN classifier, and if it works well enough, perhaps no need to go much further. If you need to squeeze out a couple additional percentage points maybe try a different simple and robust ML classifier (random forest, xgboost, or a simple two layer network). All these methods, including your current classifier, will get better with additional data and minor tuning in the future.
- dakshgupta 2y agoThank you, I will try this. I suspect we can extract some universal theory of nits and have a base filter to start with, and have it learn per-company preferences on top of that.
- keepingscore 2y agoYou should be able to do that already by taking all of your customers nit embeddings and averaging them to produce a point in space that represents the universal nit. Embeddings are really cool and the fact that they still work when averaging is one of their cool properties.
- dakshgupta 2y agoThis is a cool idea - I’ll try this and add it as an appendix to this post.