> The main advantage of a blackbox ML solution is shorter development time to a useful performance level.
I think that's kinda true but kinda false. Its true that deep learning often makes feature engineering moot. however, a lot of deep learning projects takes machine learning engineers and applied scientists along with a host of other support engineers + hardware costs ( I seen people at work say: I only used a 16 GPU instances over a couple weeks of training )
meanwhile, I consider gradient boosting fairly interpretable and they can get pretty close results with a lot less tweaking and training time. If you want to go full non-black box, logistic regression with l1 penalization and only a little bit of feature engineering often does really well, probably a lot less development time / cost compared to those high cost PHD research scientists.
> Its true that deep learning often makes feature engineering moot. however, a lot of deep learning projects takes machine learning engineers and applied scientists along with a host of other support engineers + hardware costs
DL's reduction of the required feature/model engineering is a big deal for difficult problem domains where the cost of a mistake is low. That doesn't mean you don't still benefit from adding development resources, it's just that the development cost/performance tradeoff is still typically better than with a similar-performing explainable solution. I hope this will change in the future.
> ..., logistic regression with l1 penalization and only a little bit of feature engineering often does really well,...
While I agree that Lasso is far more explainable than DL, its explainability rapidly degrades as the useful feature dimension increases and it requires significant feature engineering for good performance on difficult problems.
I would disagree with that statement. There are many ways at getting to what the model is doing, feature importances, SHAP, etc. It's not as clear as logistic regression or a single decision tree, but its not a black box either.
I think that's kinda true but kinda false. Its true that deep learning often makes feature engineering moot. however, a lot of deep learning projects takes machine learning engineers and applied scientists along with a host of other support engineers + hardware costs ( I seen people at work say: I only used a 16 GPU instances over a couple weeks of training )
meanwhile, I consider gradient boosting fairly interpretable and they can get pretty close results with a lot less tweaking and training time. If you want to go full non-black box, logistic regression with l1 penalization and only a little bit of feature engineering often does really well, probably a lot less development time / cost compared to those high cost PHD research scientists.