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

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

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

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