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.