- An introduction to Explainable AI (XAI) and Explainable Boosting Machines (EBM) - Jun 16, 2021.
Understanding why your AI-based models make the decisions they do is crucial for deploying practical solutions in the real-world. Here, we review some techniques in the field of Explainable AI (XAI), why explainability is important, example models of explainable AI using LIME and SHAP, and demonstrate how Explainable Boosting Machines (EBMs) can make explainability even easier.
- Shapash: Making Machine Learning Models Understandable - Apr 2, 2021.
Establishing an expectation for trust around AI technologies may soon become one of the most important skills provided by Data Scientists. Significant research investments are underway in this area, and new tools are being developed, such as Shapash, an open-source Python library that helps Data Scientists make machine learning models more transparent and understandable.
- Explainable and Reproducible Machine Learning Model Development with DALEX and Neptune - Aug 27, 2020.
With ML models serving real people, misclassified cases (which are a natural consequence of using ML) are affecting peoples’ lives and sometimes treating them very unfairly. It makes the ability to explain your models’ predictions a requirement rather than just a nice to have.
- KDnuggets™ News 20:n19, May 13: Start Your Machine Learning Career in Quarantine; Will Machine Learning Engineers Exist in 10 Years? - May 13, 2020.
Also: The Elements of Statistical Learning: The Free eBook; Explaining "Blackbox" Machine Learning Models: Practical Application of SHAP; What You Need to Know About Deep Reinforcement Learning; 5 Concepts You Should Know About Gradient Descent and Cost Function; Hyperparameter Optimization for Machine Learning Models
- Explaining “Blackbox” Machine Learning Models: Practical Application of SHAP - May 6, 2020.
Train a "blackbox" GBM model on a real dataset and make it explainable with SHAP.
- Explaining Black Box Models: Ensemble and Deep Learning Using LIME and SHAP - Jan 21, 2020.
This article will demonstrate explainability on the decisions made by LightGBM and Keras models in classifying a transaction for fraudulence, using two state of the art open source explainability techniques, LIME and SHAP.
- Interpretability part 3: opening the black box with LIME and SHAP - Dec 19, 2019.
The third part in a series on leveraging techniques to take a look inside the black box of AI, this guide considers methods that try to explain each prediction instead of establishing a global explanation.
- 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.
- 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.
- “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.
- 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.
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- 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.
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