Today, we take a look behind the scenes! How does my writing land on the web? We take a technical perspective where I give you an overview of the coding pipeline and a more creative perspective, where I explain the “workflow” behind a DS-Econ post!
For this blog’s series “Code Snippet Repository” I made use of git’s subtree module, nbconvert and cell tags to automatically extract python codes from jupyter notebooks and push those python scripts to the seperate csr_repo. In this post, I explain how you can setup a pipeline like this yourself! You can see a pic
In this short post, we are looking at how to implement plotly graphs into your own blog posts. For this, I am assuming that you use a site-generating framework that is based on either markdown or html files. For DS-Econ I am using Hexo.
In classification problems, we often want to assess the quality of our model beyond a simple metric like the models accuracy, especially if we have many different classes or they are of different importance to us. In this short article, I show you a more intuitive way to present the quality of your classification model - a color coded Confusion Matrix.
