How to Find Weaknesses in your Machine Learning Models - Sep 20, 2021.
FreaAI: a new method from researchers at IBM.
Interpretability, Machine Learning, Modeling, Statistics
- From Scratch: Permutation Feature Importance for ML Interpretability - Jun 30, 2021.
Use permutation feature importance to discover which features in your dataset are useful for prediction — implemented from scratch in Python.
Feature Selection, Interpretability, Machine Learning, Python
- 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.
AI, Deep Learning, Explainability, Gradient Boosting, Interpretability, LIME, Machine Learning, SHAP
- Machine Learning Model Interpretation - Jun 2, 2021.
Read this overview of using Skater to build machine learning visualizations.
Explainability, Interpretability, Machine Learning, Python
- The Explainable Boosting Machine - May 13, 2021.
As accurate as gradient boosting, as interpretable as linear regression.
Decision Trees, Explainability, Gradient Boosting, Interpretability, Machine Learning
Interpretable Machine Learning: The Free eBook - Apr 9, 2021.
Interested in learning more about interpretability in machine learning? Check out this free eBook to learn about the basics, simple interpretable models, and strategies for interpreting more complex black box models.
AI, Explainability, Explainable AI, Free ebook, Interpretability
- Know-How to Learn Machine Learning Algorithms Effectively - Nov 23, 2020.
The takeaway from the story is that machine learning is way beyond a simple fit and predict methods. The author shares their approach to actually learning these algorithms beyond the surface.
Algorithms, Complexity, Interpretability, Machine Learning
- Interpretability, Explainability, and Machine Learning – What Data Scientists Need to Know - Nov 4, 2020.
The terms “interpretability,” “explainability” and “black box” are tossed about a lot in the context of machine learning, but what do they really mean, and why do they matter?
Explainability, Explainable AI, Interpretability, Machine Learning
- A Deep Learning Dream: Accuracy and Interpretability in a Single Model - Sep 7, 2020.
IBM Research believes that you can improve the accuracy of interpretable models with knowledge learned in pre-trained models.
Accuracy, Deep Learning, Interpretability
- 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.
Dalex, Explainability, Explainable AI, Interpretability, Python, SHAP
- Understanding How Neural Networks Think - Jul 16, 2020.
A couple of years ago, Google published one of the most seminal papers in machine learning interpretability.
Google, Interpretability, Machine Learning
- modelStudio and The Grammar of Interactive Explanatory Model Analysis - Jun 19, 2020.
modelStudio is an R package that automates the exploration of ML models and allows for interactive examination. It works in a model agnostic fashion, therefore is compatible with most of the ML frameworks.
Analysis, Explainability, Interpretability, Machine Learning, R
- 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.
Explainability, Interpretability, Python, SHAP
- A simple and interpretable performance measure for a binary classifier - Mar 4, 2020.
Binary classification tasks are the bread and butter of machine learning. However, the standard statistic for its performance is a mathematical tool that is difficult to interpret -- the ROC-AUC. Here, a performance measure is introduced that simply considers the probability of making a correct binary classification.
Classification, Classifier, Interpretability, Machine Learning, Metrics, ROC-AUC
- Uber Has Been Quietly Assembling One of the Most Impressive Open Source Deep Learning Stacks in the Market - Jan 27, 2020.
Many of the technologies used by Uber teams have been open sourced and received accolades from the machine learning community. Let’s look at some of my favorites.
Deep Learning, Interpretability, NLP, Probability, Programming, Scalability, Uber
- 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.
Explainability, Interpretability, LIME, SHAP
- Interpretability: Cracking open the black box, Part 2 - Dec 11, 2019.
The second part in a series on leveraging techniques to take a look inside the black box of AI, this guide considers post-hoc interpretation that is useful when the model is not transparent.
Explainability, Explainable AI, Feature Selection, Interpretability, Python
10 Free Top Notch Machine Learning Courses - Dec 6, 2019.
Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.
Books, Computer Vision, Courses, Deep Learning, Explainability, Graph Analytics, Interpretability, Machine Learning, NLP, Python
- Explainability: Cracking open the black box, Part 1 - Dec 4, 2019.
What is Explainability in AI and how can we leverage different techniques to open the black box of AI and peek inside? This practical guide offers a review and critique of the various techniques of interpretability.
Explainability, Explainable AI, Interpretability, XAI
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead - Nov 20, 2019.
The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models.
Interpretability, Machine Learning, Modeling
- Choosing a Machine Learning Model - Oct 14, 2019.
Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications.
Interpretability, Kaggle, Machine Learning
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.
Bias, Interpretability, LIME, Machine Learning, Python, SHAP
This New Google Technique Help Us Understand How Neural Networks are Thinking - Jul 24, 2019.
Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors (CAVs) that takes a new angle to the interpretability of deep learning models.
Accuracy, Deep Learning, Google, Interpretability, Neural Networks
- “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.
Bias, Explainable AI, Interpretability, LIME, Machine Learning, SHAP, XAI
- Are BERT Features InterBERTible? - Feb 19, 2019.
This is a short analysis of the interpretability of BERT contextual word representations. Does BERT learn a semantic vector representation like Word2Vec?
BERT, Interpretability, NLP, Word Embeddings
- A Case For Explainable AI & Machine Learning - Dec 27, 2018.
In support of the explainable AI cause, we present a variety of use cases covering operational needs, regulatory compliance and public trust and social acceptance.
Bias, Explainable AI, Explanation, Interpretability, Machine Learning
- Machine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI - Dec 20, 2018.
We explain the key differences between explainability and interpretability and why they're so important for machine learning and AI, before taking a look at several techniques and methods for improving machine learning interpretability.
AI, Explainable AI, Explanation, Interpretability, Machine Learning
- Four Approaches to Explaining AI and Machine Learning - Dec 12, 2018.
We discuss several explainability techniques being championed today, including LOCO (leave one column out), permutation impact, and LIME (local interpretable model-agnostic explanations).
AI, Explainable AI, Interpretability, LIME, Machine Learning
- 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
Explainable AI, Interpretability, LIME, Machine Learning, SHAP
- Interpretability is crucial for trusting AI and machine learning - Nov 30, 2018.
We explain what exactly interpretability is and why it is so important, focusing on its use for data scientists, end users and regulators.
AI, Explainable AI, Explanation, Interpretability, Machine Learning, Trust
- Using Uncertainty to Interpret your Model - Nov 16, 2018.
We outline why you should care about uncertainty and discuss the different types, including model, data and measurement uncertainty and what different purposes these all serve.
Explainable AI, Interpretability, Taboola, Uncertainty
5 Machine Learning Projects You Should Not Overlook, June 2018 - Jun 12, 2018.
Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
Interpretability, Keras, Machine Learning, Model Performance, NLP, Overlook, Recurrent Neural Networks, Visualization
Interpreting Machine Learning Models: An Overview - Nov 7, 2017.
This post summarizes the contents of a recent O'Reilly article outlining a number of methods for interpreting machine learning models, beyond the usual go-to measures.
Interpretability, Machine Learning, Modeling, O'Reilly
- DataScience.com Releases Python Package for Interpreting the Decision-Making Processes of Predictive Models - May 24, 2017.
DataScience.com new Python library, Skater, uses a combination of model interpretation algorithms to identify how models leverage data to make predictions.
Datascience.com, GitHub, Interpretability, Python
- Big Data Desperately Needs Transparency - Mar 6, 2017.
If Big Data is to realize its potential, people need to understand what it is capable of, what information is out there and where every piece of data comes from. Without such transparency and understanding, it will be difficult to persuade people to rely on the findings.
Big Data, Interpretability, Transparency, Trust
- Introduction to Local Interpretable Model-Agnostic Explanations (LIME) - Aug 25, 2016.
Learn about LIME, a technique to explain the predictions of any machine learning classifier.
Algorithms, Classifier, Explanation, Interpretability, LIME, Machine Learning, Prediction
- The Myth of Model Interpretability - Apr 27, 2015.
Deep networks are widely regarded as black boxes. But are they truly uninterpretable in any way that logistic regression is not?
Deep Learning, Deep Neural Network, Interpretability, Support Vector Machines, Zachary Lipton
