8 | Yana Bromberg on getting creative with machine learning
Night Science
00:00
How to Disassemble a Data Set
If i can explain it to myself in my head, for why this result would appear, maybe with a little more forcing of my ideas, that's probably even better. Then we can pursue the question of whether that's what actually happened. But if i cannot at all explain what i'm seeing, and if the student cannot at all explaining what he or she is seeing, then we need to completely disssemble this thing and try something else. Just say, don't go down a blind alley and waste too much time. Absolutely. It also happens rarely, but it happens that we get an answer which was not like something we expected. And then it's cool.
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