Corpus ID: 9237797

Making machine learning models interpretable

@inproceedings{Vellido2012MakingML,
  title={Making machine learning models interpretable},
  author={A. Vellido and J. Mart{\'i}n-Guerrero and P. Lisboa},
  booktitle={ESANN},
  year={2012}
}
Data of different levels of complexity and of ever growing diversity of characteristics are the raw materials that machine learning practitioners try to model using their wide palette of methods and tools. The obtained models are meant to be a synthetic representation of the available, observed data that captures some of their intrinsic regularities or patterns. Therefore, the use of machine learning techniques for data analysis can be understood as a problem of pattern recognition or, more… Expand
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