10 coisas que a estatística pode nos ensinar sobre a análise de Big Data

Por mais que o ruído sobre o Big Data seja maior do que o sinal, posts como esse mostram que há uma luz no fim do túnel.

  1. If the goal is prediction accuracy, average many prediction models together. In general, the prediction algorithms that most frequently win Kaggle competitions or the Netflix prize blend multiple models together. The idea is that by averaging (or majority voting) multiple good prediction algorithms you can reduce variability without giving up bias. One of the earliest descriptions of this idea was of a much simplified version based onbootstrapping samples and building multiple prediction functions – a process called bagging (short for bootstrap aggregating). Random forests, another incredibly successful prediction algorithm, is based on a similar idea with classification trees.
  2. Know what your real sample size is.  It can be easy to be tricked by the size of a data set. Imagine you have an image of a simple black circle on a white background stored as pixels. As the resolution increases the size of the data increases, but the amount of information may not (hence vector graphics). Similarly in genomics, the number of reads you measure (which is a main determinant of data size) is not the sample size, it is the number of individuals. In social networks, the number of people in the network may not be the sample size. If the network is very dense, the sample size might be much less. In general the bigger the sample size the better and sample size and data size aren’t always tightly correlated.

10 coisas que a estatística pode nos ensinar sobre a análise de Big Data

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