Ohh, I can see now my mistake after reviewing the concept of bootstrapping
and sampling with replacement. I was assuming that the "replacement" was
made only after finishing each tree (i.e. If I was samping 2/3 of data, the
very same data could be selected again for each tree, but no element would
My question is why the full dataset is being used as default when building
each tree. That's not random forest. The main point of RF is to build each
tree with a subsample of the full dataset
On Sun, May 10, 2020, 09:50 Joel Nothman wrote:
> A bootstrap is very commonly a random draw with
A bootstrap is very commonly a random draw with replacement of equal size
to the original sample.
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When reading the documentation of Random Forest, I got the following:
```
max_samples : int or float, default=None If bootstrap is True, the number
of samples to draw from X to train each base estimator. - *If None
(default), then draw `X.shape[0]` samples.* - If int, then draw
`max_samples`
