Contents
Why do I want to use Gam for my model?
As mentioned in the intro, there are at least three good reasons why you want to use GAM: interpretability, flexibility/automation, and regularization. Hence, when your model contains nonlinear effects, GAM provides a regularized and interpretable solution – while other methods generally lack at least one of these three features.
How is GAM used in predictive modeling technique?
Mathematically speaking, GAM is an additive modeling technique where the impact of the predictive variables is captured through smooth functions which—depending on the underlying patterns in the data—can be nonlinear: We can write the GAM structure as:
Which is the best argument for a GAM?
But for simple models, we can also utilise the method = argument in geom_smooth, specifying the model formula. You can see the model is better fit to the data, but always check the diagnostics. check.gam is quick and easy to view the residual plots.
Can a GAM be used as a linear model?
You can essentially present model results from a GAM as if it were any other linear model, the main difference being that for the smooth terms, there is no single coefficient you can make inference from (i.e. negative, positive, effect size etc.).
Which is the best basis for GAM in R?
Cubic spline is also a common basis that mirrors adding polynomial terms for the covariates. We use the mgcv package in `R` to implement gam and use a built-in dataset medcare from the package catdata to illustrate gam and compare with the performance using logistic regression.
What are generalized additive models ( GAM ) used for?
In this c ontext, generalized additive models (GAM) offer a middle ground between simple models, such as those we fit with linear regression, and more sophisticated machine learning models like neural networks that usually promise superior prediction performance to simple models.
Which is the sum of smooth functions in GAM?
In other words, GAM is composed of a sum of smooth functions f () on the input. The idea is still that each input feature makes a separate contribution to the response, and these just add up, but these contributions don’t have to be strictly proportional to the inputs.