USGS uses machine learning to show large lithium potential in Arkansas

Re:USGS uses machine learning to show large lithium potentia... by jncfhnb on Wednesday October 23, 2024 @08:04AM (#41924108)

XGBoost models are random forest models. They’re also just consistently better for very little effort.

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Re:USGS uses machine learning to show large lithium potentia... by prog_1 on Wednesday October 23, 2024 @08:21AM (#41924257)

you surely mean that both are ensemble models. RFs and GBMs differ in how they fit the data

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Re:USGS uses machine learning to show large lithium potentia... by jncfhnb on Wednesday October 23, 2024 @12:34PM (#41926686)

A GBM like XGBoost is an ensemble of trees. It may be that when you load RandomForest modules they fit based on entropy or whatever the typical DecisionTree does but imo the term “random forest” should really convey nothing more than “ensemble of trees”.

I’m saying XGBoost would be a subclass of RF

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Re:USGS uses machine learning to show large lithium potentia... by tomrod on Wednesday October 23, 2024 @11:19AM (#41925931)

Not only RF, they incorporate GBM too as I understand it.

Often they are the best "just run it and forget it" but compared to tuning they don't always achieve top -- sometimes surprisingly so.

XGBoost and similar are solid first stops in model building.

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