Methods › General › Generalized Additive Models
Generalized Additive Models
The archive attaches this collection's text per method and the copies differ: 2 distinct texts across 2 of the 2 methods here. All are shown, most-carried first (a tie goes to the text carrying Papers with Code's collection boilerplate, then to the longer text); no vote is taken between them.
Text 1, carried by 1 of 2 methods:
Interpretability Methods seek to explain the predictions made by neural networks by introducing mechanisms to enduce or enforce interpretability. For example, LIME approximates the neural network with a locally interpretable model. Below you can find a continuously updating list of interpretability methods.
Text 2, carried by 1 of 2 methods:
Generalized Additive Models (GAMs) are a class of methods where the response variable depends linearly on some unknown functions of predictor variables. Below you can find a continuously updating list of GAM methods.
Methods
All 2 methods in this collection, most-tagged first. Year is the archive's introduced_year; the archive stores 2000 when it has none, shown here as “–”. Papers counts distinct papers the archive tags with the method. Click a heading to sort.
| NAM Neural Additive Model | – | 18 |
| Base Boosting | – | 2 |