[BENCHMARKING] Implement asv benchmarks similar to scikit-learn

[adam2392]

Setup and Work Needed

Currently on main, we have a few files that should serve as a boiler-plate for setting up the ASV benchmarking suite: https://github.com/neurodata/scikit-tree/blob/main/asv.conf.json, https://github.com/neurodata/scikit-tree/tree/main/benchmarks.

What really is needed is:

i) an end-to-end demonstration of asv benchmarking on a PR branch vs say main showing that our setup is correct
ii) a documentation for how to run the ASV benchmarks
iii) [nice-to-have] if it is possible to have a simple Docker container that runs the ASV benchmarks that we can launch w/ a very simple command on any Linux-computer, that would be nice. The Docker container should probably have some fixed-amount of RAM, and CPU resources assigned to it too.

Experiments to include

Prolly use similar simulation setup they have and Increase feature dimensionality and sample dimensionality and run a separate benchmark for each tree/forest method.

We can leverage these results to eventually compare against what is in sklearn.