My favorite ML result was (details may be inaccurate, I’m trying to recall from memory) a model that analyzed scan images from MRI machines, that would have far more confidence of the problems it was detecting if the image was taken on a machine with an old manufacture date. The training data had very few negative results from older machines, so the assumption that an image taken on an old machine showed the issue fit the data.
There was speculation about why that would happen in the training data, but the pattern noticing machine sure noticed the pattern.
My favorite ML result was (details may be inaccurate, I’m trying to recall from memory) a model that analyzed scan images from MRI machines, that would have far more confidence of the problems it was detecting if the image was taken on a machine with an old manufacture date. The training data had very few negative results from older machines, so the assumption that an image taken on an old machine showed the issue fit the data.
There was speculation about why that would happen in the training data, but the pattern noticing machine sure noticed the pattern.