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Julia for Data Science by Zacharias Voulgaris PhD

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CHAPTER 9: Sampling Data and Evaluating Results

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In the era of big data, sampling has become a popular and essential part of the data science pipeline. Even if you can get all the available data to fit into a large data structure, it may be ill-advised (unless you already have an adequate model at your disposal). Just because the cloud and large computer clusters make it possible, doesn’t mean that you should use all the available data as-is. You shouldn’t even need all of your data to see whether a feature holds value; a sample can make the whole process much more efficient.

Equally important is the evaluation of the results of your models. ...

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