Interpretable Machine Learning Using LIME Framework - Kasia Kulma (PhD), Data Scientist, Aviva

Name: Interpretable Machine Learning Using LIME Framework - Kasia Kulma (PhD), Data Scientist, Aviva
Uploaded: 2017-12-19
Duration: 37 min
Description: Aviva data scientist Kasia Kulma explains why black-box models need interpretability tools, and how the LIME framework explains individual predictions.

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Overview

In this talk, Kasia Kulma, a data scientist at Aviva, explains why black box machine learning models need interpretability tools and walks through the LIME framework (Local Interpretable Model-agnostic Explanations). She covers the accuracy versus interpretability trade-off, how LIME works under the hood by perturbing data around a single observation and fitting a simple local model to explain it, and demos it on a breast cancer classification model built with H2O AutoML. She also covers real-world stakes, including GDPR's right to an explanation and examples where models learned the wrong signal entirely.

Key takeaways

Why black box models like neural nets and gradient boosted trees are hard to trust, and why tools like feature importance and partial dependency plots fall short, especially for explaining a single prediction
How LIME actually works: it perturbs an observation's data to build a local fake dataset, scores similarity to the original, runs the black box model's predictions on it, then fits a simple local linear model to produce explanation weights
The husky versus wolf example, where a model was 94% accurate but had actually learned to detect snow in the background rather than the animal, a failure interpretability caught that accuracy alone would have missed
A live demo applying LIME to a breast cancer classification model trained with H2O AutoML, showing which cell features (size, shape, mitosis count) drove correct predictions and which drove misclassifications

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