One Model to Rule Them All

From the article: "IMHO the following topics are completely undervalued and deserve way more attention from the machine learning community:"

  • Problem Formulation: Translate a problem into a prediction or pattern recognition problem.
  • Data-Generating Process: Understand the data, its limitations and suitability for solving the problem.
  • Model Interpretation: Analyze the model beyond cross-validated performance estimates.
  • Application Context: Reflect how the model will interact with the world.
  • Model Deployment: Integrate the model into a product or process.



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