1. Parametric and Non-Parametric Methods
- Parametric methods : Make an assumption about the functional form or shape of \(f\).
- Non-Parametric methods : Do not make explicit assumptions about the functional form of \(f\).
2. Flexibility and Interpretability
- Flexibility : The flexibility of a model can be described as how much is model's behavior influenced by characteristics of the data.
- So, if flexibility increase when we increase df(degree of freedom or model complexity).
- Less flexible(Restricitve) models have more interpritability.
- Considering only prediction, the most flexible model is prefable.
In other words, flexibility and interpretability are cross-relationships, and the analysis of parameters and the model's predictive performance are selected according to the importance.
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