Decent programming skills, strong math and stats knowledge, and amazing visuals are not enough for a data science position in the industry. These are just necessary tools you will need for doing your daily tasks, but you don't have to lose the ultimate goal "to provide valuable information to decision-makers" (Duh!). This is how you can make a difference and companies know it. [5min read]
We can not continue treating our models as black boxes anymore. Remember, nobody trusts computers for making a very important decision (yet!). That's why the interpretation of Machine Learning models has become a major research topic. SHAP is a very robust approach for providing interpretability to any machine learning model. For multi-classification problems, however, documentation and examples are not very clear. [8min read]
Given a prediction on a particular example, how sure is Random Forest about it? For answering this question it is necessary to look beyond usual performance metrics and dive into the swampy waters of the confidence interval estimation for statistical learning algorithms 😖. [6 min read] (updated 11/21/22)
Sometimes notebooks are not enough and you will need to deploy your machine learning model into company infrastructre. The task involves a lot of Software Ingenieering knowledge, BUT with Plumber package for R you can do the basics with not so much pain 😉. [6 min read]
The processeses and the methods followed in Academia for evaluating a Machine Learning Model are different from the approaches used by the Industry. Why? [4min read]
