Transparency Python Packages

interpret-core

Fit interpretable models. Explain blackbox machine learning.

984K 7K 784

interpret

Fit interpretable models. Explain blackbox machine learning.

473K 7K 784

aplr

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

325K 25 5

accelerator

The Accelerator is a tool for fast and reproducible processing of eBay-scale datasets on a single computer.

30K 4 2

cot-reports

cot_reports is a Python library for fetching the Commitments of Trader reports of the Commodity Futures Trading Commission (CFTC). The following COT reports are supported: Legacy Futures-only, Legacy Futures-and-Options Combined, Supplemental Futures-and-Options Combined, Disaggregated Futures-only, Disaggregated Futures-and-Options Combined, Traders in Financial Futures (TFF) Futures-only and Traders in Financial Futures (TFF) Futures-and-Options Combined.

14K 191 54

shapash

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

10K 3K 386

raitap

Fully integrated pipeline to assess the transparency & robustness of AI models

2K 5 1

agent-alignment-protocol

Agent Alignment Protocol — the missing trust layer for the agent stack

2K 7 0

model-card-toolkit

A toolkit that streamlines and automates the generation of model cards

2K 452 89

openpolicedata

The OpenPoliceData (OPD) Python library is the most comprehensive centralized public access point for incident-level police data in the United States. OPD provides easy access to 550+ incident-level datasets from 236 police agencies and 11 entire states. Types of data include traffic stops, use of force, officer-involved shootings, and complaints.

1K 50 14