hi
there is something i cannot understand . I don’t see why we need a cross val set . i think the test error is a good measure of how the model is doing . Yes , when we train our model on training data and measure the performance on that same set of data it is misleading because the model has already learnt to fit these data but when coming to choose a model based on test set errors i don’t see why do we need an extra set to measure the final performance because it’s not about the fact that we introduce a new parameter ( for example degree here) but the point is that the model had never seen these test data so i think it’s a good way to measure its performance …
i don’t know i can’t see a strong logical reason for why we need that and just saying " the error is likely to be an optimistic estimate of generalization error " isn’t convincing for me
Hi @abdou_brk ,
Here Andrew is tackling the model selection technique is order to get a better performance of you model.
In your screenshot, polynomial model is used at different order d (d=1, d=2, …). Then automatically, the model selection technique will try to find the best order d to use for your data prediction.
This bring a refinement and should improve the performance of your prediction.
Don’t forget, machine learning is also about finding the right model to use and its underlying hyper parameters. There is no one such super model that will fit all type of data.
Kader
Deeplearning.ai Mentor