In my previous post I posted part 1 (of 3) of a Machine Learning quiz. If you have not read that post, please do, cast your vote, then come back and try part 2 below.
QUIZ, part 2
Just as a quick reminder, the image below shows the rock facies predicted from two models, which I just called A and B. Both were trained to predict the same labeled rock facies, picked by a geologist on core, which are shown on the left columns (they are identical) of the respective model panels. The right columns in each panels are the predictions.
*** Please notice that the models in this figure are (very slightly) different from part 1 because I’d forgotten to set the random seed to be the same in the two pipelines (yes, it happens, my apology). But they are not so different, so I left the image in part 1 unchanged and just updated this one.
Please answer the first question: which model predicts the labeled facies “better” (visually)?
Now study the performance as summarized in the confusion matrices for each model (the purple arrows indicate to which model each matrix belongs; I’ve highlighted in green the columns where each model does better, based on F1 (you don’t have to agree with my choice), and answer the second question (notice the differences are often a few 1/100s, or just one).
If I’m not mistaken, there is a theoretical log relationship in geologic deposition between bedding thickness and their frequency. Thick beds are an order of magnitude less frequent than their thinner counterparts. This could be a measure of accuracy of these data for “geologic correctness”
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