Package: PIE 1.0.0
PIE: A Partially Interpretable Model with Black-Box Refinement
Implements a novel predictive model, Partially Interpretable Estimators (PIE), which jointly trains an interpretable model and a black-box model to achieve high predictive performance as well as partial model. See the paper, Wang, Yang, Li, and Wang (2021) <doi:10.48550/arXiv.2105.02410>.
Authors:
PIE_1.0.0.tar.gz
PIE_1.0.0.zip(r-4.5)PIE_1.0.0.zip(r-4.4)PIE_1.0.0.zip(r-4.3)
PIE_1.0.0.tgz(r-4.5-any)PIE_1.0.0.tgz(r-4.4-any)PIE_1.0.0.tgz(r-4.3-any)
PIE_1.0.0.tar.gz(r-4.5-noble)PIE_1.0.0.tar.gz(r-4.4-noble)
PIE_1.0.0.tgz(r-4.4-emscripten)PIE_1.0.0.tgz(r-4.3-emscripten)
PIE.pdf |PIE.html✨
PIE/json (API)
| # Install 'PIE' in R: |
| install.packages('PIE', repos = c('https://misstiny.r-universe.dev', 'https://cloud.r-project.org')) |
- winequality - Wine Quality Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 24 days agofrom:ca2c6b06d6. Checks:8 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 28 2025 |
| R-4.5-win | OK | Jan 28 2025 |
| R-4.5-mac | OK | Jan 28 2025 |
| R-4.5-linux | OK | Jan 28 2025 |
| R-4.4-win | OK | Jan 28 2025 |
| R-4.4-mac | OK | Jan 28 2025 |
| R-4.3-win | OK | Jan 28 2025 |
| R-4.3-mac | OK | Jan 28 2025 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| data_process: process tabular data into the format for the PIE model. | data_process |
| MAE: Mean Absolute Error | MAE |
| PIE: A Partially Interpretable Model with Black-box Refinement | PIE-package PIE |
| PIE: Partially Interpretable Model | PIE_fit |
| Make Predictions for PIE | predict.PIE |
| RPE: Relative Prediction Error | RPE |
| sparsity_count | sparsity_count |
| Wine Quality Data | winequality |
