GAM for Regression

GAM, or Generalized Additive Model, is a flexible statistical tool used to predict an outcome based on multiple factors. Unlike simple methods that assume relationships are straight lines, GAM can model complex, curved relationships by applying smooth functions to each factor. Think of it as fitting gentle curves to data, capturing intricate patterns without over-complicating the model. This approach helps understand how different variables contribute to the outcome, making predictions more accurate and insights clearer. Essentially, GAM offers a balance between flexibility and interpretability in regression analysis.