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| Both sides previous revisionPrevious revisionNext revision | Previous revisionNext revisionBoth sides next revision | ||
| data_mining:error_analysis [2018/05/21 19:30] – [False positive data] phreazer | data_mining:error_analysis [2018/05/21 19:38] – [Working on most promising problems] phreazer | ||
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| Bigger network almost always improves bias and more data improves variance (//not necessarily a tradeoff// between the two). | Bigger network almost always improves bias and more data improves variance (//not necessarily a tradeoff// between the two). | ||
| - | ===== Working on most promising problems ===== | + | ====== Working on most promising problems |
| Best case performance if no false positives? | Best case performance if no false positives? | ||
| Line 130: | Line 130: | ||
| Result: Calc percentage of problem category (potential improvement " | Result: Calc percentage of problem category (potential improvement " | ||
| + | ====== Misslabeled data ====== | ||
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| + | DL algos: If % or errors is //low// and errors are //random//, they are robust | ||
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| + | Add another col " | ||
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| + | Principles when fixing labels: | ||
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| + | - Apply same process to dev and test set (same distribution) | ||
| + | - Also see what examples algo got right (not only wrong) | ||
| + | - Train and dev/test data may come from different distribution (no problem if slightly different) | ||