pyGAM: balancing interpretability and predictive power using... - Dani Servén Marín

[@masonholcombe3327]

smoothing having a similar closed form solution as ridge regression is so satisfying

[@rajavelks6861]

I'm eager to see how PyGAM is going to be helpful for my projects (having some unordered categorical predictors), given that it has been heavily inspired by mgcv package. Thanks a lot, Dani for bringing in a relief for Python users.

[@TAP7a]

Honestly I would put decision trees as even more interpretable than linear models, but only a little more predictive. You can put a decision tree rendering in front of a lay person and I think most would be able to accurately interpret what it means, whereas I remember from high school that probably 30-50% of my classmates found y = mx + c to be absolutely terrifying

[@guynyamsi7729]

Hello, and thank you for this package. I wanted to add an offset to my model, but I realize that it's not possible. An offset can be seen as a linear predictor with a variable having a fixed coefficient of 1 (g(mu) = f(x1) +x2 ) : x2 is an offset. Please, is it possible to fix the value of the coefficient in a linear term? For example, l(1, coef_estimate=1)? In this case, variable 1 (x2) will behave like an offset.