Compiled evaluation of regression decision trees

Summary:

In some use cases, predicting given a model is in the hot-path, so
speeding up decision tree evaluation is very useful.

An effective way of speeding up evaluation of decision trees can be to
generate code representing the evaluation of the tree, compile that to
optimized object code, and dynamically load that file via dlopen/dlsym
or equivalent.

See

<https://courses.cs.washington.edu/courses/cse501/10au/compile-machlearn.pdf>

for a detailed discussion, and

<http://tullo.ch/articles/decision-tree-evaluation/>

for some benchmarks with C++.

It's fairly trivial to implement this for regression trees due to the
simpler `predict()` method, but in theory is possible do to it for
arbitrary multi-class, multi-output trees.

This diff deals only with the simple case of single-output regression
trees. There are a few changes that need to be made to allow it to work
on Linux (change the CXX compiler invocation flags), and Windows (use
the appropriate equivalents of dlopen, etc).

Tradeoffs:
Since it's purely opt-in from a users perspective and fairly short in
terms of lines of code (~40 lines of Cython, <300 lines of codegen and
evaluation Python code), I wasn't sure if the additional complexity was
worth it.

Testing:
This is fairly easy to test on a given platform, since we just need to
verify that the predictions output by the compiled model exactly match
those of the interpreted model.

Benchmarks:
For random forests, we see 5x to 8x speedup in evaluation.
For gradient boosted ensembles, it's between a 1.5x and 3x speedup in
evaluation. This is due to the fact that gradient boosted trees already
have an optimized prediction implementation.

There is a benchmark script attached that allows us to examine the
performance of evaluation across a range of ensemble configurations and
datasets.

In the graphs attached, GB is "Gradient Boosted", RF is "Random Forest",
"D1", "D3" is "max-depth=1", "max-depth=3", and "B10" is
"max_leaf_nodes=10".