Notebook here.  Helper functions here.

One of the best features of Random Forests is that it has built-in Feature Selection.  Explicability is one of the things we often lose when we go from traditional statistics to Machine Learning, but Random Forests lets us actually get some insight into our dataset instead of just having to treat our model as a

Part 1 (n_estimators) here
Part 2 (max_depth) here
Notebook here

Another parameter, another set of quirks!

min_samples_leaf is sort of similar to max_depth.  It helps us avoid overfitting.  It's also non-obvious what you should use as your upper and lower limits to search between.  Let's do what we did last week - build a forest

Continued from here

Notebook for this post is here

Binary search code itself is here

max_depth is an interesting parameter.  While n_estimators has a tradeoff between speed & score, max_depth has the possibility of improving both.  By limiting the depth of your trees, you can reduce overfitting.

Unfortunately, deciding on upper & lower bounds is less than

Ah, hyperparameter tuning.  Time & compute-intensive.  Frequently containing weird non-linearities in how changing a parameter changes the score and/or the time it takes to train the model.

RandomizedSearchCV goes noticeably faster than a full GridSearchCV but it still takes a while - which can be rough, because in my experience you do still need to be iterative with it