Rotational invariance is the property of producing equivalent model behavior when input representations are rotated. The available treatment defines the property but does not explain how to implement, test, or measure it. section 5.2.3
Rotational invariance
Rotational invariance is the property of producing equivalent model behavior when input representations are rotated.
How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it
- How deep it goes
- Defines equivalent behavior under rotated inputs but stops short of implementation, evaluation, or mathematical treatment.
- What you can do after
- Evaluate whether a model conceptually preserves behavior when input representations are rotated.
Related concepts
- gradient boostingcontrasts with · Gradient boosting is an ensemble-learning technique that sequentially adds predictive models, typically weak learners, to reduce a loss by fitting its gradient.
This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.