1 References used in this class assigment.

1 References used in this class assigment.

1.1 Theory

1.1.1 Books and articles

An Introduction to Statistical Learning

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

Understanding Random Forest: How the Algorithm Works and Why it Is So Effective

Tony Yiu

What Random Forests Tell Us About Democracy

Pablo Duboue

Random Forest — A Model Designed to Provide Structure in Chaos

Pranov Mishra

Tune Machine Learning Algorithms in R (random forest case study)

Jason Brownlee

1.1.2 Wikipedia

Akaike Information Criterion

Kolmogorov–Smirnov Test

Multicollinearity

Receiver Operating Characteristic

1.2 R Programing

R for Data Science

Garrett Grolemund, Hadley Wickham

Top 50 ggplot2 Visualizations - The Master List (With Full R Code)

Selva Prabhakaran

caret Package

Max Kuhn

radomForest Package

Andy Liaw

hmeasure Package

David J. Hand

1.3 Awesome functions from the community

Some awesome functions we found in the internet and adapted for this class assignment.

Stack Overflow: @Axeman