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?
This post is co-authored by Jiahang Zhong, Head of Data Science at Zopa. Zopa is a UK-based digital bank and peer to peer (P2P) lender. In 2005, Zopa launched the first ever P2P lending company to gi ...
If you have worked with any kind of forecasting models, you will know how laborious it can be at times especially when trying to predict multiple variables. From identifying if a time-series is…
In our last post we took a broad look at model observability and the role it serves in the machine learning workflow. In particular, we discussed the promise of model observability & model monitoring…
A broad overview of the sub-field of machine learning interpretability; conceptual frameworks, existing research, and future directions.
A groundbreaking and relatively new discovery upends classical statistics with relevant implications for data science practitioners and…
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By KDnuggets -
2020-11-04
Interpretability, Explainability, and Machine Learning – What Data Scientists Need to Know
By Amazon Web Services -
2021-02-19
How Zopa enhanced their fraud detection application using Amazon SageMaker Clarify
Time-Series Forecasting with Google BigQuery ML
The Model’s Shipped; What Could Possibly go Wrong
By The Gradient -
2020-11-21
Interpretability in Machine Learning: An Overview
By Medium -
2021-01-04
Something Every Data Scientist Should Know But Probably Doesn’t: The Bias-Variance Trade-off…