Category Archives: Interpretability

Properties of Interpretability

In my last two posts, I wrote about model interpretability, with the goal of trying to understanding what it means and how to measure it. In the first post, I described the disconnect between our mental models and algorithmic models, … Continue reading

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Goals of Interpretability

In my last post, I looked at the gap that arises when we delegate parts of our thought processes to algorithmic models, rather than incorporating the rules they identify directly into our mental models, like we do with traditional statistics. I … Continue reading

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Interacting with ML Models

The main difference between data analysis today, compared with a decade or two ago, is the way that we interact with it. Previously, the role of statistics was primarily to extend our mental models by discovering new correlations and causal … Continue reading

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