Considering that random forests have many layers and beat deep learning in most applications, maybe we just need to rebrand them as deep forests and they'll be the next big thing.
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With a sufficiently large datasets and complex features deep learning outperforms on tabular data by miles. Most people don’t have access do that much right tabular data so it might not be as well known.
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That's the point. The very long tail of small problems involve not much data and not very rich features.
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You don’t get to decide what is in bounds and what isn’t, Pedro. The community spent decades on small data problems and got precisely nowhere. If anything is the niche now, it’s trying to solve new problems with old methods like random forests.
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Some of us have already gone down the scaling-up path and found where it hits the wall. Others just don’t get it (and are ignorant of ML history, as well).
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