GAM is a weighted sum ofa.n degree polynomial where n varies from 1 to 3b.features

It seems like you're asking about Generalized Additive Models (GAMs). Here's a step-by-step explanation:

1. GAMs are a type of statistical model used for regression problems, where you're trying to predict a continuous outcome variable based on one or more predictor variables.

2. The basic idea of a GAM is that it's a weighted sum of functions of the predictor variables. These functions can be non-linear, which makes GAMs more flexible than traditional linear regression models.

3. In your question, you're asking about a GAM where the functions are polynomials of degree 1 to 3. This means that each predictor variable can be transformed by a linear function (degree 1), a quadratic function (degree 2), or a cubic function (degree 3) before being included in the model.

4. The "features" in your question are the predictor variables. Each feature can be transformed by a different polynomial function, and the GAM will learn the best degree of polynomial for each feature based on the data.

5. The "weighted sum" part of your question refers to how the GAM combines the transformed features to make a prediction. Each transformed feature is multiplied by a weight (which the GAM learns from the data), and the weighted features are then summed together to produce the prediction.

6. So, to summarize, a GAM is a model that predicts an outcome variable as a weighted sum of polynomial functions of the features, where the degree of the polynomial can vary from 1 to 3.