Model Simplification
Definition
Model simplification reduces unnecessary complexity of a model.
This can include fewer features, lower tree depth, or a simpler model class. The goal is a reasonable balance of quality, interpretability, and effort.
Simply explained
A complicated plan is reduced to what is needed for the task.
Why it matters in practice
Simpler models can be easier to verify and cheaper to operate.
Example from business
Hypothetical example: An operation replaces a hard-to-explain model with a sufficiently good small decision tree.
Limits and pitfalls
- Over-simplification overlooks important relationships. Quality must be tested on separate data.
Origin & history
The principle belongs to statistical model selection and machine learning.
Use in mid-sized companies
- SMEs especially benefit with small datasets and decisions requiring explanation.
Frequently asked questions
Is simplification the same as compression?
It overlaps with it but can also concern the model selection itself.
Sources
- Decision Trees — · Scikit-learn: Decision Trees —
Editorially reviewed on: