I'm estimating forest biomass using Random Forest regression at a large scale. I want to train the Random Forest model using as many samples as possible (~100,000 samples), but it seems the Google Earth Engine can only train RF using no more than 5,000 samples. I wonder if there's a solution for this rather than split the study area.

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    Please be weary of the path you are on. It is a misnomer that random forests cannot overfit. If there is a model assumption in weak learners it is that there is heterogeneity in the ensemble. With the number of samples you are thinking, you may very well end up with strong correlation in the ensemble and on overfit model. – Jeffrey Evans Nov 29 '20 at 23:36
  • Are your training samples distributed equally across all of your classes? – Aaron Nov 30 '20 at 20:04
  • Hi Aaron, the samples are equally distributed. – Dong Nov 30 '20 at 21:24
  • Thanks Jeffrey, I train random forest at a very large scale, so I think 5,000 samples are not enough. If 100,000 are too many, I would train the model use less samples (say 20,000). However, I failed to train the model with more than 5,000 samples. – Dong Nov 30 '20 at 21:27

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