interpret-core
Fit interpretable models. Explain blackbox machine learning.
Decision gist · record as of 2026-08-14
Yes. interpret-core is actively maintained, has low install friction, and fills a genuine need for interpretable ML in regulated and high-stakes domains. The license treatment is unclear—verify the actual license before use in proprietary contexts—but the package itself is stable and has no known vulnerabilities. Install if you need interpretability alongside accuracy.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction installation with a pure Python wheel.
- Active maintenance with recent releases and solid community engagement (6916 stars).
License · maintenance · safety
(unclear) — License treatment is unclear; the package metadata does not specify an SPDX identifier or raw license string. Verify the actual license terms in the repository before adopting in proprietary or regulated contexts.
last release 2026-03-17 (150 days) · last repo commit 2026-08-10 · 6,916 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 966,463 downloads/mo, #4,618 on PyPI
Alternatives
Verify before relying
pip install interpret-core
from interpret.glassbox import ExplainableBoostingClassifier
ebm = ExplainableBoostingClassifier()
ebm.fit(X_train, y_train)
from interpret import show
show(ebm.explain_global())- Whether the unclear license permits use in proprietary or commercial applications.
- Performance characteristics on datasets larger than typical in-memory workloads.
- Compatibility with GPU acceleration or distributed training frameworks beyond Azure SynapseML.
What it is and what it does
interpret-core is a unified framework for training interpretable models and explaining blackbox machine learning systems. It implements state-of-the-art techniques including Explainable Boosting Machines (EBMs)—a modern take on Generalized Additive Models that achieve accuracy comparable to random forests and gradient boosting while remaining fully interpretable—alongside explainers for existing models like SHAP, LIME, and partial dependence analysis. The package is built on numpy, pandas, scikit-learn, and joblib, making it a natural fit for standard data science workflows.
interpret-core addresses critical use cases in model debugging, feature engineering, fairness detection, regulatory compliance, and high-risk domains like healthcare and finance. You can train glassbox models directly, explain individual predictions, compare multiple models side-by-side, and optionally apply differential privacy for sensitive data. The framework handles pandas DataFrames and numpy arrays natively and includes support for pairwise interactions by default.
Use it for
- Train an Explainable Boosting Machine for classification or regression when you need both accuracy and full model transparency.
- Debug why a blackbox model made a specific prediction using SHAP or LIME explainers on existing models.
- Detect fairness issues by analyzing global feature importance and local prediction explanations across demographic groups.
- Satisfy regulatory requirements in finance or healthcare by producing exact, auditable explanations for individual decisions.
- Engineer better features by understanding which variables drive model predictions and how they interact.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
interpret-core is actively maintained, has low install friction, and fills a genuine need for interpretable ML in regulated and high-stakes domains. The license treatment is unclear—verify the actual license before use in proprietary contexts—but the package itself is stable and has no known vulnerabilities. Install if you need interpretability alongside accuracy.
Install
interpret-core on PyPI
Before you install
Low friction installation with a pure Python wheel. Active maintenance with recent releases and solid community engagement (6916 stars). Four runtime dependencies—numpy, pandas, scikit-learn, joblib—are standard data science libraries.
Requires Python 3.10 or later.
License in practice
License treatment is unclear; the package metadata does not specify an SPDX identifier or raw license string. Verify the actual license terms in the repository before adopting in proprietary or regulated contexts.
Quickstart
pip install interpret-core
from interpret.glassbox import ExplainableBoostingClassifier
ebm = ExplainableBoostingClassifier()
ebm.fit(X_train, y_train)
from interpret import show
show(ebm.explain_global())Verify before relying
- Whether the unclear license permits use in proprietary or commercial applications.
- Performance characteristics on datasets larger than typical in-memory workloads.
- Compatibility with GPU acceleration or distributed training frameworks beyond Azure SynapseML.
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpypandasscikit-learnjoblib |
| Maintenance | Actively maintained 150 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 966,463 / month, #4,618 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: interpret_core-0.7.8-py3-none-any.whl
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