Programming 7 Generalized Additive Models
ANSWER
Hint 3: FastSparse
- Description: FastSparse is a package that implements fast classification techniques for sparse generalized linear and additive models in R.
- Installation: You can install FastSparse by following the instructions provided in this GitHub repository: https://github.com/jiachangliu/fastSparse/tree/main/installation.
- Usage: Example code on how to run FastSparse can be found in this repository: https://github.com/jiachangliu/fastSparse/tree/main/application_and_usage.
- Visualization: Example code for visualizing shape functions with FastSparse is available here: https://github.com/jiachangliu/fastSparse/tree/main/visualization.
Hint 4: pygam
- Description: PyGAM is a package that allows you to fit Generalized Additive Models (GAM) and is available for Python.
- GitHub Repository: You can find the pygam package here: https://github.com/dswah/pyGAM.
If you have any specific questions or need further information about any of these packages or tools, please feel free to ask.
QUESTION
Description
usage are available in https://github.com/interpretml/interpret and https://interpret.ml/docs/
ebm.html.
Hint 3: FastSparse implements fast classification techniques for sparse generalized linear and additive models
in R. To install this package, please follow the instruction here https://github.com/jiachangliu/
fastSparse/tree/main/installation. Example code about how to run FastSparse is available https:
//github.com/jiachangliu/fastSparse/tree/main/application_and_usage and example code to visualize
shape functions is https://github.com/jiachangliu/fastSparse/tree/main/visualization.
Hint 4: You may also use pygam package to fit a GAM model which is available here https://github.com/
dswah/pyGAM.
![Place Your Order Here](http://scholarywriters.com/wp-content/uploads/2023/08/Bottom-of-every-post.png)