Sparse generalized additive models (GAMs) are a way to make more accurate predictions by allowing for non-linear relationships between variables. The new method called reluctant generalized additive modeling (RGAM) helps build these models more efficiently by prioritizing linear features over non-linear ones. RGAM works well with different types of data like binary, count, and survival data. The method has been shown to be effective in both real-world and simulated scenarios.