Whitepaper
Cracking the Box: Interpreting
Black-Box Machine Learning Models
As AI and machine learning get adopted across nearly every industry, it becomes increasingly important to keep ML interpretable. The decisions algorithms make should be human- and business-understandable, to avoid bias and uncertainty.
Since it is critical for businesses to explain decisions being taken by ML models, black-box machine learning models present a unique challenge for ML Engineers.
In this whitepaper, we will examine:
- Classes of interpretability methods
- Methods of ML interpretability, including
- Partial dependency plot
- Permutation importance
- SHAP
- LIME
- Anchor
- Pros, cons, features, and specifics of ML interpretability methods
ML models can be quite hard to explain and interpret. Luckily, data scientists, ML researchers, and ML engineers come up with new methods of interpretability of ML models. Get to know about some of them now!
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As AI and machine learning get adopted across nearly every industry, it becomes increasingly important to keep ML interpretable. The decisions algorithms make should be human- and business-understandable, to avoid bias and uncertainty.
Since it is critical for businesses to explain decisions being taken by ML models, black-box machine learning models present a unique challenge for ML Engineers.
In this whitepaper, we will examine:
- Classes of interpretability methods
- Methods of ML interpretability, including
- Partial dependency plot
- Permutation importance
- SHAP
- LIME
- Anchor
- Pros, cons, features, and specifics of ML interpretability methods
ML models can be quite hard to explain and interpret. Luckily, data scientists, ML researchers, and ML engineers come up with new methods of interpretability of ML models. Get to know about some of them now!
