Today, I worked on my project findings and continued working diligently on my project, which involves analyzing the CDC 2018 data. And we develop the code.
and I also watched the videos of Train Error and Test error in depth.
Train error:
Train error is typically used during the training phase to optimize the model’s parameters to minimize this error. The goal is to make the model perform as well as possible on the training data.
Test Error:
Test error is a critical metric because it provides an estimate of how well the model is likely to perform on real-world, unseen data. The lower the test error, the better the model’s generalization ability.