Jupyter and Rstudio notebooks have become the default standard for data science development. However, it is important to know their limitations and detect the moment of moving to a more "powerful" tool. Since in data science a very significant portion of the work is related to development, it is always important to be aware of the last development tools.
The U-shape observed when measuring model performance on testset as a function of its flexibility does not hold during training deep learning models. (WHAT!!!?????. Is the world going mad?. Not really.)
Some useful resources for playing with epidemic models (Mostly COVID-19 of course). From an explanation of the dificulties behind building an epidemic models to the different approaches followed by researchers and enthusiasts (me) here in my home town, Mendoza City.
Beware of Random Forest GINI index for feature importance. Some other resources related with feature selection such as how to use PCA and the problems (or not) behind colinearity.
