Blog

Jul 31, 2018 · newsletter

Progress in machine learning interpretability

Our goal when we do research is to address capabilities and technologies that we expect to become production-ready in one to two years. That focus on fast-moving areas means that new algorithmic ideas sometimes come along that allow our clients to extend or improve upon the work in our reports.

We published our report on machine learning interpretability last year. The technical focus of our report was LIME, a tool that computes locally correct explanations of a model’s behaviour. If a model is good, LIME’s explanations can offer completely new insights. (We saw this in our prototype, which models customer churn using traditional machine learning techniques, but then uses LIME to say precisely what it is about a customer that makes them a churn risk.) And if a model is bad, LIME can help you understand why.

This all sounds great, but we had to leave three issues unresolved in our report. Progress since last year has begun to address those concerns.

LIME explanations of sentiment classification. “Not” is a positive word in one example, but not in another. Image credit: Anchors.

Firstly, LIME’s explanations are local. For example, a LIME explanation may (correctly) tell you that “This movie is not bad” has positive sentiment because it contains the word “not.” But because LIME’s explanations are local, a user is not generally entitled to conclude from this that the word “not” always indicates positive sentiment. This makes sense: the presence of “not” in “this movie is not very good” does not tell you its sentiment is positive! But how local is “local”? How similar to the original sentence does a new sentence need to be for LIME’s explanation to apply?

Anchors explanations of sentiment classification. “Not” is a positive word in combination with “bad.” Image credit: Anchors.

The creators of LIME offer an answer to this question in the form of Anchors: High-Precision Model-Agnostic Explanations(PDF, 2.7MB).” Anchors works like LIME in that it probes the behaviour of the black-box model by perturbing the original example. But it takes a very different approach to constructing a human-friendly explanation. Rather than fit a locally correct linear model (which raises the question: how local?), it constructs a set of rules. For the “this movie is not bad” example above, the rule might be “sentence contains ‘not’ and ‘bad’". Such black and white rules are easier for many people to understand than quantitative weights. And they implicitly define locality: if the sentence doesn’t contain “not” or “bad,” the rule (and the explanation) doesn’t apply. The Anchors code is publicly available.

SHAP explanation of a prediction for a model of the Boston house price dataset.

Secondly, LIME’s choice of perturbation strategy and its local linear model are heuristics – which is to say they feel a little arbitrary, and it’s reasonable to wonder whether they are optimal in practice. In A Unified Approach to Interpreting Model Predictions Lundberg and Lee carefully define what we mean by optimal, and show that LIME is a specific example of a more general class of explanation tools they call “additive feature attribution methods.” This class includes the classical “Shapley” feature importance measure familiar to economists, and DeepLIFT, a neural network interpretability tool. They unify this class in a provably optimal way they call SHAP. The code is public, and is highly optimized for the particular case of tree-based methods such as XGboost. One thing we really like about SHAP is that the built-in visualization tools are very nice! This seemingly minor point is surprisingly important to the adoption of new tools, and we’re glad to see these authors spend time on this aspect of their code.

Finally, how do we test explanations? How do we know whether an explanation is evidence of a problem with the model or a surprising insight? Patrick Hall and colleagues at H2O.ai sum up the current situation very well in a new article for O’Reilly Testing machine learning interpretability techniques. The conclusion is: “use more than one type of tool to explain your machine learning models, and look for consistent results across different explanatory methods.” We agree, and we’re glad to see new options such as Anchors and SHAP that make this easy!

So, a year after our report, machine learning interpretability remains not only a very useful business capability, but a vibrant area of research.

Read more

Newer
Aug 15, 2018 · scifi
Older
Jul 31, 2018 · newsletter

Latest posts

Jun 22, 2020 · post

How to Explain HuggingFace BERT for Question Answering NLP Models with TF 2.0

by Victor · Given a question and a passage, the task of Question Answering (QA) focuses on identifying the exact span within the passage that answers the question. Figure 1: In this sample, a BERTbase model gets the answer correct (Achaemenid Persia). Model gradients show that the token “subordinate ..” is impactful in the selection of an answer to the question “Macedonia was under the rule of which country?". This makes sense .. good for BERTbase.
...read more
Jun 16, 2020 · notebook

Evaluating QA: Metrics, Predictions, and the Null Response →

by Melanie · A deep dive into computing QA predictions and when to tell BERT to zip it! In our last post, Building a QA System with BERT on Wikipedia, we used the HuggingFace framework to train BERT on the SQuAD2.0 dataset and built a simple QA system on top of the Wikipedia search engine. This time, we’ll look at how to assess the quality of a BERT-like model for Question Answering.
qa.fastforwardlabs.com
May 19, 2020 · notebook

Building a QA System with BERT on Wikipedia →

by Melanie · So you’ve decided to build a QA system. You want to start with something simple and general so you plan to make it open domain using Wikipedia as a corpus for answering questions. You want to use the best NLP that your compute resources allow (you’re lucky enough to have access to a GPU) so you’re going to focus on the big, flashy Transformer models that are all the rage these days.
qa.fastforwardlabs.com
Apr 28, 2020 · notebook

Intro to Automated Question Answering →

by Melanie · Welcome to the first edition of the Cloudera Fast Forward blog on Natural Language Processing for Question Answering! Throughout this series, we’ll build a Question Answering (QA) system with off-the-shelf algorithms and libraries and blog about our process and what we find along the way. We hope to wind up with a beginning-to-end documentary that provides:
qa.fastforwardlabs.com
Apr 1, 2020 · newsletter

Enterprise Grade ML

by Shioulin · At Cloudera Fast Forward, one of the mechanisms we use to tightly couple machine learning research with application is through application development projects for both internal and external clients. The problems we tackle in these projects are wide ranging and cut across various industries; the end goal is a production system that translates data into business impact. What is Enterprise Grade Machine Learning? Enterprise grade ML, a term mentioned in a paper put forth by Microsoft, refers to ML applications where there is a high level of scrutiny for data handling, model fairness, user privacy, and debuggability.
...read more
Apr 1, 2020 · post

Bias in Knowledge Graphs - Part 1

by Keita · Introduction This is the first part of a series to review Bias in Knowledge Graphs (KG). We aim to describe methods of identifying bias, measuring its impact, and mitigating that impact. For this part, we’ll give a broad overview of this topic. image credit: Mediamodifier from Pixabay Motivation Knowledge graphs, graphs with built-in ontologies, create unique opportunities for data analytics, machine learning, and data mining. They do this by enhancing data with the power of connections and human knowledge.
...read more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)

Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
Notebooks

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com
Notebook

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.
https://colab.research.google.com/drive/1pjPzsw_uZew-Zcz646JTkRDhF2GkPk0N

About

Cloudera Fast Forward is an applied machine learning reseach group.
Cloudera   Blog   Twitter