Python Libraries for Interpretable Machine Learning - Sep 4, 2019.
In the following post, I am going to give a brief guide to four of the most established packages for interpreting and explaining machine learning models.
Tags: Bias, Interpretability, LIME, Machine Learning, Python, SHAP
- Opening Black Boxes: How to leverage Explainable Machine Learning - Aug 1, 2019.
A machine learning model that predicts some outcome provides value. One that explains why it made the prediction creates even more value for your stakeholders. Learn how Interpretable and Explainable ML technologies can help while developing your model.
Tags: Explainable AI, Feature Selection, LIME, Machine Learning, SHAP, XAI
- “Please, explain.” Interpretability of machine learning models - May 9, 2019.
Unveiling secrets of black box models is no longer a novelty but a new business requirement and we explain why using several different use cases.
Tags: Bias, Explainable AI, Interpretability, LIME, Machine Learning, SHAP, XAI
2018’s Top 7 Python Libraries for Data Science and AI - Jan 21, 2019.
This is a list of the best libraries that changed our lives this year, compiled from my weekly digests.
Pages: 1 2
Tags: AI, AutoML, Data Science, Python, SHAP, spaCy
- Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies - Dec 6, 2018.
The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.
Pages: 1 2
Tags: Explainable AI, Interpretability, LIME, Machine Learning, SHAP
