What is a Generalized Additive Model (GAM)?

hard

Answer

$g(E[Y])\; = \;{\beta}_{0}\; + \;f_{1}(x_{1})\; + \;f_{2}(x_{2})$
$Extends GLM by letting each feature enter through a non-parametric smooth.$
$Interpretable (partial dependence plot per feature), captures non-linearity without explicit interactions.$
$Fit via penalized splines (mgcv in R, pygam in Python).$
$Modern uses: interpretable ML (competes with GBDT on tabular), forecasting (Prophet is a GAM).$

Check yourself — multiple choice

Only linear
g(E[Y]) = Σ $f_{j}(x_{j})$ with smooths $f_{j}$ ; extends GLM to non-linear per feature; interpretable + competitive with GBDT on tabular; e.g. Prophet
Same as GLM
Not real

GAM: sum of smooth functions per feature; interpretable non-linearity.

#regression#glm

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