🚀 Cross-validation is used to train machine learning models.
- ✅ Better accuracy: it helps evaluate the true performance of algorithms and avoid overfitting.
- 🔍 Improved quality control: it ensures models are more reliable before deployment.
- 💡 Resource optimization: it reduces errors and costs by validating with more representative data.
Innovation is not only about creating models, but ensuring they work 🌍.
More information at the link 👇
Also published on LinkedIn.
