treeinterpreter
Package for interpreting scikit-learn's decision tree and random forest predictions.
Decision gist · record as of 2026-08-14
Yes, if you are working with tree models and need local prediction explanations on a stable codebase. The package is simple, dependency-free, and has no known vulnerabilities. However, do not use it if you depend on active maintenance or need compatibility with very recent versions—test thoroughly before deploying to production, as the project is abandoned.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires scikit-learn 0.17 or later; no Python version constraint is documented.
- Low friction installation with no runtime dependencies.
- However, the package is abandoned—last release was 2021-01-10 and last commit 2023-07-18—so expect no bug fixes or updates.
License · maintenance · safety
BSD (permissive) — BSD license is permissive, allowing commercial and private use with minimal restrictions; you may use and modify the code freely as long as you retain the license notice.
last release 2021-01-10 (2042 days) · last repo commit 2023-07-18 · 759 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 103,532 downloads/mo, #12,801 on PyPI
Alternatives
Verify before relying
pip install treeinterpreter
from treeinterpreter import treeinterpreter as ti
rf = RandomForestRegressor()
rf.fit(trainX, trainY)
prediction, bias, contributions = ti.predict(rf, testX)- Whether the package remains compatible with scikit-learn 0.17+ across current versions.
- Python version support beyond the unspecified classifier.
- Whether the package works with the eight tree model types listed in the description.
What it is and what it does
TreeInterpreter is a small library that breaks down predictions from tree-based models into interpretable components. For each prediction, it computes a bias term and a contribution value for each input feature, so you can see exactly how much each feature pushed the prediction up or down. This is useful when you need to explain individual predictions or debug why a model made a particular decision.
The package has no runtime dependencies and installs as a pure Python wheel. It is no longer maintained, with the last release in 2021-01-10 and no activity since 2023-07-18, so it may not work with newer versions without modification.
Use it for
- Explain individual tree predictions to stakeholders by showing feature contributions.
- Debug model behavior by decomposing predictions and identifying which features dominate decisions.
- Audit model fairness by examining whether sensitive features have outsized contributions.
- Validate model logic by checking that prediction = bias + sum(contributions) holds across data.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working with tree models and need local prediction explanations on a stable codebase.
The package is simple, dependency-free, and has no known vulnerabilities. However, do not use it if you depend on active maintenance or need compatibility with very recent versions—test thoroughly before deploying to production, as the project is abandoned.
Install
treeinterpreter on PyPI
Before you install
Low friction installation with no runtime dependencies. However, the package is abandoned—last release was 2021-01-10 and last commit 2023-07-18—so expect no bug fixes or updates.
Requires scikit-learn 0.17 or later; no Python version constraint is documented.
License in practice
BSD license is permissive, allowing commercial and private use with minimal restrictions; you may use and modify the code freely as long as you retain the license notice.
Quickstart
pip install treeinterpreter
from treeinterpreter import treeinterpreter as ti
rf = RandomForestRegressor()
rf.fit(trainX, trainY)
prediction, bias, contributions = ti.predict(rf, testX)Verify before relying
- Whether the package remains compatible with scikit-learn 0.17+ across current versions.
- Python version support beyond the unspecified classifier.
- Whether the package works with the eight tree model types listed in the description.
Package facts
| License | BSD permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Abandoned 2,042 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 103,532 / month, #12,801 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: BSD LicenseNatural Language :: EnglishProgramming Language :: Python |
Evidence: treeinterpreter-0.2.3-py2.py3-none-any.whl
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