Mathematical formula for Generalized Additive Model in Python
The fundamental concept of GAMs lies in the ability of the response variable to be described as an average of components that are smooth functions of the predictors. This is so that each predictor can independently affect the response in a possibly nonlinear manner although the model as a whole remains interpretable because of the form in which the equation is additive.
The aforementioned model can be described with the help of the following formula:
[Tex]y = \beta_0 + \sum f_i(x_i) + \epsilon[/Tex], where
β0 is the constant; fi(xi) smooth functions of the predictors; xi and ϵ – the errors.
Generalized additive model in Python
Generalized additivemodels Models are a wider and more flexible form of a linear model with nonparametric terms and are simply extensions of generalized linear models. Whereas simple linear models are useful when relationships between two variables are strikingly linear, all of which might not be possible in the real world, generalized additive models are advantageous in that they can simultaneously capture non-linear relationships between two variables. In
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