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Which is the best definition of generalized additive model?
Generalized additive model. In statistics, a generalized additive model (GAM) is a generalized linear model in which the linear predictor depends linearly on unknown smooth functions of some predictor variables, and interest focuses on inference about these smooth functions.
How to optimize the generalized additive model with AIC?
An alternative is to select the smoothing parameters to optimize a prediction error criterion such as Generalized cross validation (GCV) or the Akaike information criterion (AIC). Finally we may choose to maximize the Marginal Likelihood (REML) obtained by integrating the model coefficients,
How are generalized linear models used in logis Tic regression?
You are familiar, of course, from your regression class with the idea of transforming the response variable, what we’ve been calling Y, and then predicting the transformed variable from X. This was not what we did in logis- tic regression.
How are Gams different from generalised linear models?
A GAM is a linear model with a key difference when compared to Generalised Linear Models such as Linear Regression. A GAM is allowed to learn non-linear features. GAMs relax the restriction that the relationship must be a simple weighted sum, and instead assume that the outcome can be modelled by a sum of arbitrary functions of each feature.
What was the original GAM fitting method used?
The original GAM fitting method estimated the smooth components of the model using non-parametric smoothers (for example smoothing splines or local linear regression smoothers) via the backfitting algorithm.
Which is an extension of a generalised linear model?
This is basically all there is too it – an extension of generalised linear models (GLMs) with a smoothing function. Of course, there may be many sophisticated things going on when you fit a model with smooth terms, but you only need to understand the rationale and some basic theory.
Which is an example of a basis expansion?
Basis expansion implies a basis function. In mathematics, a basis function is an element of a particular basis for a function space. For example, sines and cosines form a basis for Fourier analysis and can duplicate any waveform shape (square waves, sawtooth waves, etc.) just by adding enough basis functions together.
When to use basis expansion in machine learning?
For machine learning “basis expansion” is merely a fancy way of saying “adding more linear terms to the model.”. The term is, for example, used precisely once in BOOSTING ALGORITHMS: REGULARIZATION, PREDICTION AND MODEL FITTING By Peter Buhlmann and Torsten Hothorn, so that if you missed the meaning entirely, you would not be out by much.