Introduction to PIE | Installation | Getting Started | Generate Data | Fitting PIE | Predicting PIE | Evaluate PIE
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-4.7-any)PIE_1.0.0.zip(r-4.6-any)PIE_1.0.0.zip(r-4.5-any)
PIE_1.0.0.tgz(r-4.6-any)PIE_1.0.0.tgz(r-4.5-any)
PIE_1.0.0.tar.gz(r-4.7-any)PIE_1.0.0.tar.gz(r-4.6-any)
PIE_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PIE/json (API)
| # Install 'PIE' in R: |
| install.packages('PIE', repos = c('https://misstiny.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- 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 from:ca2c6b06d6. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 128 | ||
| source / vignettes | OK | 179 | ||
| linux-release-x86_64 | OK | 135 | ||
| macos-release-arm64 | OK | 193 | ||
| macos-oldrel-arm64 | OK | 170 | ||
| windows-devel | OK | 89 | ||
| windows-release | OK | 96 | ||
| windows-oldrel | OK | 81 | ||
| wasm-release | OK | 97 |
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 |
