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Interpreting Machine Learning Models

medium.com

Many machine learning textbooks present students with a chart that shows a tradeoff between model interpretability and model accuracy. This is a heuristic, but many students come away thinking that this tradeoff is as strict as a law of physics.

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A national effort was undertaken to build Algorithms to predict which pneumonia patients should be admitted to hospitals and which are treated as outpatients. Only by interpreting the model was a crucial problem discovered and avoided. Understanding why a model makes a prediction can literally be...

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  • Global Interpretability: How well can we understand the relationship between each feature and the predicted value at a global level — for our entire observation set. Can we understand both the magnitude and direction of the impact of each feature on the predicted va...

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An ordinary least squares (OLS) model generates coefficients for each feature. These coefficients are signed, allowing us to describe both the magnitude and direction of each feature at the global level. For local interpretability, we need only multiply the coefficient vector by a specific featur...

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In the middle of the accuracy-interpretability spectrum are random forests. We’ve often seen them described as “black boxes,” which we think this is unfair — maybe “gray” but certainly not “black”!

Random forests are collections of decision trees, like the one drawn below. The splits in eac...

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As the hottest topic in machine learning over the past decade, we’d be remiss if we didn’t mention neural networks. Hailed for outstanding accuracy in difficult domains like image recognition and language translation, they’ve also generated criticism for lacking interpretability.

Nobody...

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