You're facing disappointing machine learning results. How do you navigate presenting them effectively?
When machine learning experiments don't go as planned, it's crucial to present the findings constructively. Here's how to turn those results into opportunities:
- Frame the results within the context of learning and growth, emphasizing what can be improved next time.
- Detail specific data points that led to these outcomes, providing a clear analysis.
- Suggest actionable steps for future research, fostering a proactive discussion.
How do you approach sharing less-than-ideal outcomes in your work?
You're facing disappointing machine learning results. How do you navigate presenting them effectively?
When machine learning experiments don't go as planned, it's crucial to present the findings constructively. Here's how to turn those results into opportunities:
- Frame the results within the context of learning and growth, emphasizing what can be improved next time.
- Detail specific data points that led to these outcomes, providing a clear analysis.
- Suggest actionable steps for future research, fostering a proactive discussion.
How do you approach sharing less-than-ideal outcomes in your work?