Here is a (growing) collection of my thinking on machine learning, data science, and AI, written up as blog posts as I explore the space.
Class Imbalance. In classification problems (where we predict discrete outcomes such as loan default vs non-default, or customer churn vs retention), most ML frameworks perform best when classes are relatively balanced. However, in real-world business settings, this is often not the case, and is known as class imbalance, and it can cause serious challenges for standard modelling approaches. I've split this topic up into three sections:
Firstly, I explore what class imbalance is, and how it happens, and what the main challenges are.
Secondly, I look into data sampling approaches you can take to correct for class imbalance in your data, and
Thirdly, I look at model-based approaches you can use to solve class imbalance in your datasets and modelling projects.