Re:A watershed moment for protein structure prediction
by ovi256 on Monday January 20, 2020 @10:07AM
(#22099281)
That's only true in some limited cases.
Section 4.1 of the paper he linked discusses extensively the limitations of linear model interpretability. Here's an example:
"With respect to algorithmic transparency, [the claim that linear models are more interpretable]
seems uncontroversial, but given high dimensional or heavily engineered features, linear models lose simulatability or decomposability, respectively."
Given how many interesting problems are high-dimensional or solved through heavy feature engineering, you must be lucky to live in the happy space where problems are either low-dimensional enough to not need heavy feature engineering or where the client is already familiar with the high-dimensional features you're going to limit the model to using.
Section 4.1 of the paper he linked discusses extensively the limitations of linear model interpretability. Here's an example:
"With respect to algorithmic transparency, [the claim that linear models are more interpretable] seems uncontroversial, but given high dimensional or heavily engineered features, linear models lose simulatability or decomposability, respectively."
Given how many interesting problems are high-dimensional or solved through heavy feature engineering, you must be lucky to live in the happy space where problems are either low-dimensional enough to not need heavy feature engineering or where the client is already familiar with the high-dimensional features you're going to limit the model to using.