interpret
Fit Interpretable Machine Learning Models
Description
Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).
Downloads
379
Last 30 days
11010th
1.2K
Last 90 days
3.5K
Last year
Trend: -1.3% (30d vs prior 30d)
24
Last 30 days
117
Last 90 days
678
Last year
Trend: -40% (30d vs prior 30d)
0
Last 7 days
11
Last 30 days
0
All-time
autoCRAN-only: this name is served only by autoCRAN, so the count is exact.
CRAN Check Status
Show all 13 flavors
| Flavor | Status |
|---|---|
| r-devel-linux-x86_64-debian-clang | OK |
| r-devel-linux-x86_64-debian-gcc | OK |
| r-devel-linux-x86_64-fedora-clang | OK |
| r-devel-linux-x86_64-fedora-gcc | OK |
| r-devel-windows-x86_64 | OK |
| r-oldrel-macos-arm64 | OK |
| r-oldrel-macos-x86_64 | OK |
| r-oldrel-windows-x86_64 | OK |
| r-patched-linux-x86_64 | OK |
| r-release-linux-x86_64 | OK |
| r-release-macos-arm64 | OK |
| r-release-macos-x86_64 | OK |
| r-release-windows-x86_64 | OK |
Check History
OK 12 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Apr 25, 2026
NOTE 11 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
installed package size
installed size is 7.5Mb
sub-directories of 1Mb or more:
libs 7.4Mb
installed package size
installed size is 7.9Mb
sub-directories of 1Mb or more:
libs 7.8Mb
Code
Structure
Lines of code
45,037
Files
115
Compiled share
98.1%
Has compiled src
Yes
Language breakdown
API
Exported functions
3
Internal functions
31
Recent export changes
Testing & CI
Has tests
No
Test-to-code ratio
0.00
testthat edition
–
CI present
No
CI type
[]
PR gated
No
Docs
Return-value doc rate
100%
\dontrun example ratio
0%
Roxygen coverage
100%
Has pkgdown
No
NEWS present
No
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
–
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
3.0.0
System requirements
1
C++ standard
C++17
License
MIT + file LICENSE
License flags
SPDX valid, OSI approved
History
Versions
12
First release
2019-10-06
Latest release
2026-03-03
Avg cadence
21 days
Cold removal rate
100%
Dep drift
0
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/cran-code-metrics.