My Reading List: Data Science
Live post with a curated list of high-quality data science posts and videos I found enlightening.
Mapping Categorical Predictors to Numeric With Target Encoding
Target encoding is commonly used to map categorical variables to numeric with the objective of facilitating exploratory data analysis and machine learning modeling. This post covers the basics of this method, and explains how and when to use it.
Everything You Don't Need to Know About Variance Inflation Factors
Deep explanation of what Variance Inflation Factors (VIF) are, how they work, what they really mean, and how they are used to manage multicollinearity in linear models.
Multicollinearity Hinders Model Interpretability
In this post, I delve into the intricacies of model interpretation under the influence of multicollinearity, and use R and a toy data set to demonstrate how this phenomenon impacts both linear and machine learning models.
Designing R functions to compute betadiversity indices from species lists
This is a tutorial written for R users needing to compute betadiversity indices from species lists rather than from presence-absence matrices, and for R beginners or intermediate users that want to start using their own functions.
Setup of a shared folder in a home cluster
In the previous posts I have covered how to setup a home cluster, and how to run parallel processes with foreach in R. However, so far I haven’t covered how to setup a folder shared among the cluster nodes to store the results of parallel computations.
