tune

I was listening to episode 135 of ‘Not so standard deviations’ - Moderate confidence The hosts, Hilary and Roger talked about when to use tidymodels packages and when not. Here are my 2 cents for when I think it makes sense to use these packages and when not: When not you are always using GLM models. (they are very flexible!) it makes no sense to me to go for the extra {parsnip} layer if you are always using the same models. [Read More]

NETWORKS, PREDICT EDGES Can we predict if two nodes in the graph are connected or not? But let’s make it very practical: Let’s say you work in a social media company and your boss asks you to create a model to predict who will be friends, so you can feed those recommendations back to the website and serve those to users. You are tasked to create a model that predicts, once a day for all users, who is likely to connect to whom. [Read More]

So you want to compete in a kaggle competition with R and you want to use tidymodels. In this howto I show how you can use CatBoost with tidymodels. I give very terse descriptions of what the steps do, because I believe you read this post for implementation, not background on how the elements work. This tutorial is extremely similar to my previous post about using lightGBM with Tidymodels. Why tidymodels? [Read More]

So you want to compete in a kaggle competition with R and you want to use tidymodels. In this howto I show how you can use lightgbm (LGBM) with tidymodels. I give very terse descriptions of what the steps do, because I believe you read this post for implementation, not background on how the elements work. Why tidymodels? It is a unified machine learning framework that uses sane defaults, keeps model definitions andimplementation separate and allows you to easily swap models or change parts of the processing. [Read More]