Here's how you can address feedback on your machine learning model requiring significant changes.
Receiving feedback that your machine learning (ML) model needs significant changes can be daunting. But it's an opportunity to refine your work and push the boundaries of what your model can achieve. By addressing feedback systematically, you can enhance your model's performance and applicability. Whether it's adjusting algorithms, adding data, or rethinking the problem scope, every piece of feedback is a step towards a more robust model. Embrace the challenge with a clear strategy, and you'll find that addressing feedback is an integral part of the machine learning journey.
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Dishant ZaveriSeeking Summer 2025 internships | Master's in CS @Texas A&M | Winner of Singapore India International Hackathon 23…
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Marco NarcisiCEO | Founder | AI Developer at AIFlow.ml | Google and IBM Certified AI Specialist | LinkedIn AI and Machine Learning…
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