Overfitting - Wikipedia
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2026-07-16 09:16:37
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划选高亮2026-07-16 15:40:37
原文高亮摘录
“Overfitting is especially likely in cases where learning was performed too long or where training examples are rare”
Whisper 随想笔记
Yeah that's why my model nails the test set but dies on new data, classic.
划选高亮2026-07-16 12:37:37
原文高亮摘录
“To lessen the chance or amount of overfitting, several techniques are available”
Whisper 随想笔记
But early stopping feels like a hack—sometimes it just masks a bad architecture.
划选高亮2026-07-16 12:28:37
原文高亮摘录
“To lessen the chance or amount of overfitting, several techniques are available”
Whisper 随想笔记
Regularization saved my models so many times, honestly a lifesaver.
划选高亮2026-07-16 09:34:37
原文高亮摘录
“overfitting is the production of an analysis that corresponds too closely or exactly to a particular set of data”
Whisper 随想笔记
I've seen this in my stock predictions—great past data, useless future calls.
划选高亮2026-07-16 09:25:37
原文高亮摘录
“overfitting is the production of an analysis that corresponds too closely or exactly to a particular set of data”
Whisper 随想笔记
Wait, isn't that just what happens when you try too hard to fit a line through every point?
划选高亮2026-07-16 09:16:37
原文高亮摘录
“overfitting is the production of an analysis that corresponds too closely or exactly to a particular set of data”
Whisper 随想笔记
So it's basically memorizing the test answers instead of learning the subject.
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