Decision tree learning - Wikipedia
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2026-07-22 10:03:31
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Text Highlight2026-07-22 16:27:31
Original Highlight Excerpt
"Bootstrap aggregated (or bagged) decision trees, an early ensemble method, builds multiple decision trees by repeatedly resampling training data with replacement, and voting the trees…"
Whisper Note
Bagging always felt like magic to me—just resample and vote, and it works way better.
Text Highlight2026-07-22 13:24:31
Original Highlight Excerpt
"Classification tree analysis is when the predicted outcome is the class (discrete) to which the data belongs."
Whisper Note
But what if the data overlaps? Then it's not so clear-cut.
Text Highlight2026-07-22 13:15:31
Original Highlight Excerpt
"Classification tree analysis is when the predicted outcome is the class (discrete) to which the data belongs."
Whisper Note
So it's like sorting things into boxes, got it.
Text Highlight2026-07-22 10:21:31
Original Highlight Excerpt
"Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity"
Whisper Note
They're cute for demos, but give me a linear model for real work.
Text Highlight2026-07-22 10:12:31
Original Highlight Excerpt
"Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity"
Whisper Note
Honestly I find random forests way more reliable in practice.
Text Highlight2026-07-22 10:03:31
Original Highlight Excerpt
"Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity"
Whisper Note
Yeah but they overfit like crazy without pruning, that's the catch.
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