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Not really. What the article says is, the model would predict 50% of time whether a company will fail or not, which doesn't make sense, because 50% for a binary prediction (i.e. fail or not) is exactly nothing.

So maybe it's just bad or confusing wording in the article, the guy actually meant to say something else.

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Yeah I'm going with confusing wording, I think he meant what I said above. Not to mention he could be referring to eliminating false positives, which is slightly different to finding true positives versus true negatives.

I think it's easier to relate to a coin flip if we use an "unfair coin".

90% of the time the coin flip returns tails (aka fail).

10% of the time it returns heads (aka win).

For a given coin flip, their algorithm can predict the results 50% of the time. At this point I don't remember the calculations off the top of my head, but it involves a Binomial distribution.

Hmm, I can predict that 90% of the time, so I don't follow.

That prediction doesn't improve over the prior. So its no better than the (biased) coin flip.

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