Explainability and Interpretability to Develop Reliable ML models
Shapash is a Python library dedicated to the interpretability of Data Science models. It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can more easily understand their models, share their results and easily document their projects in an HTML report. End users can understand the suggestion proposed by a model using a summary of the most influential criteria.
ML based QSAR Modelling And Translation of Model to Deployable WebApps
...Link : https://pubs.acs.org/doi/10.1021/acs.jcim.4c02372
- Simple to use and no compromise on essential features necessary to make reliable QSAR models.
- From Generating Reliable ML Based QSAR Models to Developing Your Own QSAR WebApp.
For any feedback or queries, contact
kabeermuzammil614@gmail.com
- Available on Windows and Linux
- Software Authorship - Muzammil Kabier
-If You are Facing Issues in Deployment to Streamlit, Try 'requirements.txt' in the Github repo or The Files Deposited Here.
Extensible agent framework with the ability to perform common tasks such as notify you when you have a mail, or a message in your phone, or some web or file has change... A kind of automated webapp secretary.