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| Both sides previous revisionPrevious revisionNext revision | Previous revisionLast revisionBoth sides next revision | ||
| data_mining:error_analysis [2018/05/21 19:27] – [Basic recipe for training NNs] phreazer | data_mining:error_analysis [2018/05/21 22:11] – [Problems with different train and dev/test set dist] 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). | ||
| - | ===== False positive data ===== | + | ====== Working on most promising problems ====== |
| Best case performance if no false positives? | Best case performance if no false positives? | ||
| Line 127: | Line 127: | ||
| Create spread sheet: Image / Problem | Create spread sheet: Image / Problem | ||
| - | Calc percentage of problem category | ||
| + | Result: Calc percentage of problem category (potential improvement " | ||
| + | |||
| + | General rule: Build your first system quickly, then iterate (dev/test setup, build system, bias/ | ||
| + | ====== Misslabeled data ====== | ||
| + | |||
| + | DL algos: If % or errors is //low// and errors are //random//, they are robust | ||
| + | |||
| + | Add another col " | ||
| + | |||
| + | Principles when fixing labels: | ||
| + | |||
| + | * 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) | ||
| + | |||
| + | ====== Missmatched train and dev/test set ====== | ||
| + | |||
| + | * 200.000 high qual pics | ||
| + | * 10.000 low qual blurry pics | ||
| + | |||
| + | * Option 1: Combine images, random shuffle in train/ | ||
| + | * Advantage: Same distribution | ||
| + | * Disadvantage: | ||
| + | * Option 2: | ||
| + | * Train set: 205.000 with high and low qual; Dev & Test: 2500 low quality | ||
| + | * Advantage: Optimizing right data | ||
| + | * Disadvantage: | ||
| + | |||
| + | ====== Problems with different train and dev/test set dist ====== | ||
| + | |||
| + | Not always good idea to use different dist in train and dev | ||
| + | |||
| + | * Human error ~ 0 | ||
| + | * Train 1% | ||
| + | * Dev 10% | ||
| + | |||
| + | Training-dev set: same distribution as training set, but not used for training | ||
| + | |||
| + | * Train 1% | ||
| + | * Train-dev: 9% | ||
| + | * Dev: 10% | ||
| + | |||
| + | Still high gap between train and train-dev => variance problem | ||
| + | |||
| + | If Train and Train-dev would be closer => data-mismatch problem. | ||
| + | |||
| + | Summary: | ||
| + | * Human level 4% | ||
| + | * Avoidable bias | ||
| + | * Train 7% | ||
| + | * Variance | ||
| + | * Train-dev: 10% | ||
| + | * Data mismatch | ||
| + | * Dev: 12% | ||
| + | * Degree of overfitting to dev set (if to high => bigger dev set) | ||
| + | * Test: 12% | ||