Maximum-Ruin-9590
Maximum-Ruin-9590 t1_j9uzp6m wrote
My coworker has just one dataset and does cross validation, tuning and comparing on train. He gets pretty good metrics that way.
Maximum-Ruin-9590 t1_j1mzyh7 wrote
Reply to comment by Maximum-Ruin-9590 in [D] The case for deep learning for tabular data by dhruvnigam93
Btw depending on the task, you can also check out temporal fusion transformers (forecasting, nlp, Image recognition)
Maximum-Ruin-9590 t1_j1mzp41 wrote
You can write your own custom functions for tree based models.
Here is an pretty good industry show case with lightGBM. Found it on reddit:
https://doordash.engineering/2021/06/29/managing-supply-and-demand-balance-through-machine-learning/
It comments on their custom optimizer.
Maximum-Ruin-9590 t1_j1imyle wrote
Reply to [D] GPT3 Concrete applications (With python code snippets). Do you see other ones ? by AImSamy
Hope they add it to ms teams. Turn off my screen and let the gpt3 handle the stand ups.
Maximum-Ruin-9590 t1_j9v03zg wrote
Reply to comment by Maximum-Ruin-9590 in [D] Is validation set necessary for non-neural network models, too? by osedao
As mentioned u need validation sets aka some kind of folds for most things in ML. Crossvalidation and tuning just to name some things. It is also smart to have folds to compare different models with each other.