It's a classic tale of algorithmic woe. You see, we built a decision tree, and it's supposed to be all about precision and accuracy. But, as with all things human (or, you know, machine), we've managed to botch it. The numbers just don't add up, if you will.
Turns out, we've been badly underestimating the problem in more ways than one. Our algorithms are all like, "Oh, this will only take a week to fix." But, nope, it's been months, and we're still scratching our heads.
We clearly weren't thinking when we built this thing. Maybe we should just start over?
But, hey, at least it's good for a laugh. Laughing matters, right?
On the bright side, we've learned that underestimating the underestimation is a great way to... well, not estimate. Who needs precision, anyway? It's all about fuzzy logic, right?
But, seriously, we'll try to do better next time. Learning from our mistakes is the new black.