Parsimony of Models

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For models with a lot of parameters, the goodness-of-fit is very likely to be very high. However, it is also likely to generalize bad. So we need measure of generalizability

Here parsinomy gives us a few advantages.

  • easy to perceive
  • better generalizations

Planted: by ;

References:
  1. Vandekerckhove, J., & Matzke, D. (2015). Model comparison and the principle of parsimony. Oxford Library of Psychology.
Dynamic Backlinks to wiki/model-selection/parsimony-of-models:
Goodness-of-fit
Does the data agree with the model? Calculate the distance between data and model predictions. Apply …
wiki/model-selection/parsimony-of-models Links to:
Goodness-of-fit
Does the data agree with the model? Calculate the distance between data and model predictions. Apply …
Measures of Generalizability
To measure the generalization, we define a generalization error, $$ \begin{align} \mathcal G = …

L Ma (2020). 'Parsimony of Models', Datumorphism, 11 April. Available at: https://datumorphism.leima.is/wiki/model-selection/parsimony-of-models/.