Advantages of GAMs
- Flexibility:
- GAMs can capture complex, non-linear relationships between the dependent and independent variables, making them suitable for a wide range of applications.
- Interpretability:
- Unlike many machine learning models, GAMs provide interpretable results. Each predictor’s effect can be visualized individually, which helps in understanding the influence of each variable.
- Additivity:
- The additivity of GAMs simplifies the interpretation. Each term in the model can be examined separately, facilitating easier identification of the contribution of each predictor.
- Customizability:
- Different smoothing techniques (like splines) can be used for different predictors, allowing for customized fitting that can improve model performance.
- Handling Non-Linearity:
- GAMs handle non-linear relationships effectively without the need for explicitly specifying the form of the non-linearity, as required in polynomial regression.
- Reduced Overfitting:
- Smoothing functions help in controlling overfitting by regularizing the fitted functions, especially when dealing with noisy data.
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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