Disadvantages of GAMs
- Complexity:
- The flexibility of GAMs can lead to increased model complexity, making them computationally intensive and sometimes difficult to tune.
- Selection of Smoothing Parameters:
- Choosing the appropriate smoothing parameters or basis functions can be challenging and requires expertise. Improper selection can lead to underfitting or overfitting.
- Scalability:
- GAMs may not scale well with very large datasets or with a large number of predictors due to their computational intensity.
- Additive Assumption:
- The assumption of additivity might be restrictive in some cases where interactions between predictors are important. While GAMs can include interaction terms, they are generally more complex to specify and interpret.
- Interpretation of Smoothing Terms:
- While GAMs are interpretable, the smoothing terms themselves can sometimes be difficult to explain, especially to stakeholders not familiar with the methodology.
- Software and Implementation:
- Implementing GAMs requires specialized statistical software and packages (e.g.,
mgcv
in R), which might not be as widely understood or available as more standard linear or logistic regression models.
- Implementing GAMs requires specialized statistical software and packages (e.g.,
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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