These files are example code, write-ups, and general testing of new ideas in ML. Some of this information is taken from Harvard's Introduction to Data Science Course and MIT's Inference on Causal and Structural Parameters using ML and AI (which I am a student in).

Please be mindful: this is an ongoing process, there might be grammatical errors or unfinished ideas, I am currently still writing the introductions to explain the ideas (like the model specification for Lasso).

Contains code that explains RCTS with small examples

Contains code that explains the motivation of these dimension reduction techninuqes.

Info on motivating why we sample split and what cross validation is.

Info on CART, Random Forests, Boosting Bagging, ADABoosted Trees Also check out the "Austin Animal" project which uses tree based methods to prediction adoption.