| Name | Version | Summary | date |
| arize-phoenix |
5.12.0 |
AI Observability and Evaluation |
2024-11-25 19:08:28 |
| InterpreTS |
0.2.0 |
Feature extraction from time series to support the creation of interpretable and explainable predictive models. |
2024-11-21 22:22:11 |
| arize-phoenix-evals |
0.17.5 |
LLM Evaluations |
2024-11-19 21:43:36 |
| arize-otel |
0.5.3 |
Helper package for OTEL setup to send traces to Arize & Phoenix |
2024-11-01 20:44:00 |
| arize-phoenix-otel |
0.6.1 |
LLM Observability |
2024-10-17 22:55:51 |
| legrad-torch |
1.1 |
LeGrad |
2024-10-14 17:22:51 |
| xaiographs |
1.2.0 |
Python library providing Explainability & Fairness AI functionalities |
2024-10-09 10:32:34 |
| astra-logs |
4.2.8 |
AI Observability and Evaluation |
2024-06-07 14:02:04 |
| effector |
0.0.272 |
A Python library for global and regional effects |
2024-05-05 20:52:13 |
| wpdm3333 |
0.1.0 |
Warsztat Praktyka Data Mining |
2024-03-12 23:16:21 |
| template-wpdm |
0.1.0 |
Warsztat Praktyka Data Mining |
2024-03-11 10:48:41 |
| milosz7wpdm |
0.1.1 |
Warsztat Praktyka Data Mining |
2024-03-06 17:28:24 |
| knac-toolkit |
1.0.2 |
Knowledge Augmented Clustering |
2024-02-29 10:37:49 |
| pgeon-xai |
1.0.1 |
pgeon (or pgeon-xai) is a Python package that produces explanations for opaque agents using Policy Graphs (PGs) |
2024-02-25 22:09:30 |
| trustyai |
0.5.0 |
Python bindings to the TrustyAI explainability library. |
2024-02-02 13:31:44 |
| explainerdashboard |
0.4.5 |
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models. |
2023-12-17 19:42:38 |
| wmsd |
1.0.0 |
TOPSIS ranking and interpretation using WMSD-space |
2023-11-29 22:28:22 |
| timeshap |
1.0.4 |
KernelSHAP adaptation for recurrent models. |
2023-09-13 09:24:00 |
| thermostat-datasets |
1.1.0 |
Collection of NLP model explanations and accompanying analysis tools |
2023-06-26 10:15:06 |
| bhad |
0.1.0 |
Bayesian Histogram-based Anomaly Detection |
2023-05-30 09:22:53 |