Transparency
Readable by Design
Techniques that make a model or its inputs readable as they stand, so a reviewer needs no separate explanation step.
4 techniques
4 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Coefficient Magnitudes (in Linear Models) | Metric | Architecture/linear Models Paradigm/parametric +2 | Tabular | Coefficient Magnitudes explains a linear model by reading its fitted coefficients. Each coefficient shows how much the... | ||
| RuleFit | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | RuleFit builds a predictive model that a person can read: a short list of if-then rules plus a few ordinary feature... | ||
| Monotonicity Constraints | Algorithmic | Architecture/neural Networks Architecture/probabilistic/gaussian Processes +3 | Tabular | Monotonicity constraints require a model's prediction to move only one way as a chosen input rises, with the other... | ||
| Generalized Additive Models | Algorithmic | Architecture/linear Models/gam Paradigm/parametric +2 | Tabular | A generalised additive model (GAM) builds its prediction by adding a baseline and one smooth curve for each input... |
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