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Most ML models have been obliterated by LLMs but I do hope that the vast diversity of different ML techniques remains relevant. There's so much mathematical ingenuity in all the pre-LSTM approaches. SVMs, Gaussian models, Random Forest, network algorithms, ...

caleb 11mo ago

Data scientist here. I can assure you that llama are not replacing many existing ML applications, mostly new ones. Tree based models still dominate with tabular data structures (I worked in credit fraud aand we just used boosted tree algos). If llms are used in these applications to generate insights it’s usually hallucinations. If useful at all in these applications it’s them being used as orchestrators to train purpose-build classifiers regressors or networks.

After 3 years of R&D with all types of network structures and alternative algos, nothing beat a boosted tree in our client’s credit card fraud data sets

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Discussion

calle 11mo ago

random forest maxi here

caleb 11mo ago

Agreed. Random forest works out of the box, not param tuning 99.99% or the time with very acceptable results

The extra 1-2% performance you get using lgbm, xgboost or catboost it’s usually not worth it and makes your model more fragile and prone to quickly drift

Few.

MrTea 11mo ago

We found isolation by nearest neighbor ensemble (iNNE) works best for our anomaly detection application. Lots of use cases still for ML models outside of LLMs