My problem is simple. I want to train a dataset using random forest on a huge dataset (with $n$ rows). Let's assume I can only fit $b < n$ rows in memory at a time.
I see several options:
Option #1: Bag of Decision Trees
Draw a subsample of $b$ rows from the total dataset (w/o replacement). Fit a decision tree on the subsample. Repeat $s$ times. Average the results of the estimators.
Option #2: Bag of Bagged Decision Trees
Same as above but instead of a decision tree, apply bagging to the subsample.
Option #3: Bag of Random Forests
Same as above but instead, apply a random forest.
Option #4: Forest of Random Forests
Take my dataset, and split it into several subset based on several features (either by unsupervised learning or using some prior knowledge), and train random forests on each subset.
I originally suggested the idea here.
Option #5, #6, & #7: Bag of X with Resampling
Take option #1, #2, or #3, and instead of fitting an estimator on the subsample, resample a new subset from the subsample w/ replacement so that the new subset has $n$ rows.
original subset (n rows) => subsample (b rows) => resample (n rows)
Then apply the estimator to the new subset.
For option #6 and #7, I would resample for each iteration in the ensemble loop.
Option #1, #2, and #3 is called Pasting.
Option #1 is computationally the most efficient out of all the other options, but I'm worried that it's accuracy is highly dependent on the choice of $b$. Option #5 might be better, but not by very much.
Option #6 and #7 are similar to Bag of Little Bootstraps (which you can learn more about here).
Out of options above, which is the best method?
My guess is that option #7 is the best.
I found this blog post. Looks like they do option #3, mention option #1, and allude to option #7.
Best is obviously subjective. Model accuracy (test MSE) is important, but I'm willing to sacrifice some accuracy for greater computation efficiency.