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Approaches to feature selection have been developed since the 1960-es. It's a tricky problem to approach when looking for the optimal feature subset to select. In the 1968-paper, Huges demonstrated that the performance of a classifier can peak and thereafter decline - on an independent test set - when adding still more features [G.F.Hughes. On the mean ...


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I believe another difference between cosine similarity and TF-IDF is that cosine similarity is done in an embedding space, such as one created by doc2vec. Such an embedding puts words that are used in similar contexts near to each other, so you could use clustering to find similar documents. But cosine distance probably makes more sense for a couple of ...


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Feature selection is a well-studied area within Machine Learning and Pattern Recognition. In general, pruning features is a non-monotonous process. The relative importance of remaining features change when seemingly redundant features are being removed from your Twitter sentiment classifier. There are ready-to-use webservices available that perform feature ...


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