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2026-07-15 10:02:58
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Text Highlight2026-07-15 13:23:58
Original Highlight Excerpt
"There is no single learning algorithm that works best on all supervised learning problems"
Whisper Note
I mean, that's just the free lunch theorem, kind of obvious though.
Text Highlight2026-07-15 13:14:58
Original Highlight Excerpt
"There is no single learning algorithm that works best on all supervised learning problems"
Whisper Note
Truer words were never said, every dataset's a different beast.
Text Highlight2026-07-15 10:20:58
Original Highlight Excerpt
"The goal of supervised learning is for the trained model to accurately predict the output for new, unseen data."
Whisper Note
I've seen models nail the training set then choke on new data. Classic overfit.
Text Highlight2026-07-15 10:11:58
Original Highlight Excerpt
"The goal of supervised learning is for the trained model to accurately predict the output for new, unseen data."
Whisper Note
Generalization error—that's the real boss, not just fitting the training set.
Text Highlight2026-07-15 10:02:58
Original Highlight Excerpt
"The goal of supervised learning is for the trained model to accurately predict the output for new, unseen data."
Whisper Note
Yeah, but if the training data's biased, that 'unseen' data won't be so accurate.

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