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aschroeder91 t1_j6mys1h wrote

Reply to comment by qalis in [D] Have researchers given up on traditional machine learning methods? by fujidaiti

Good to hear! Do you know what the space of hybrid models looks like? Specifically using deep learning for input signal to data and classical machine learning algorithms (e.g. gradient boosted trees) for data processing.

My intuition says that hybrid models definitely have a role in general problem solving machines. I've tried searching this topic and the space is muddy at best.

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beanhead0321 t1_j6nbq1j wrote

I remember sitting in on a talk from a large insurance company who did this a number of years back. They used DL for feature engineering, but used traditional ML for the predictive model itself. This had to do with satisfying some regulatory requirements around model interpretability.

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aschroeder91 t1_j6nliim wrote

the real innovation will be comfortably backpropagating through the hybrid model as a whole

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ktpr t1_j6nmsol wrote

Did they claim traditional ML explained the features engineered by the DL? If so, how did they explain the units of feature variables?

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qalis t1_j6o6jjv wrote

The simplest way to do this is combining autoencoders (e.g. VAEs) and boosting, I have seen this multiple times on Kaggle.

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