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Tag : machine learning

In a previous blog post, I spurred some ideas on why it is meaningless to pretend to achieve 100% accuracy on a classification task, and how one has to establish a baseline and a ceiling and tweak a classifier to work the best it can, knowing the boundaries. Recapitulating what I said before, a classification task involves assigning which out of a set of categories or labels should be assigned to some data, according to some properties of the data. For example, the spam filter your email service provides assigns a spam or no spam status to every email. If ...

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Very often we get people contacting us for projects in which they envision the usage of some Machine Learning (ML) techniques to solve a specific problem they have.

Sometimes these people do not have any automated system and are solving whatever problem they currently have with human labor. In these cases, the mere fact of being able to produce anything that works reasonably well can make a huge difference for them. Imagine, for example, that your company wants you to review 100,000 tweets that make reference to its brand name, because they want to know which customers are not ...

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