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