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| data_mining:error_analysis [2018/05/21 17:46] – [Misslabeled data] phreazer | data_mining:error_analysis [2018/05/21 20:24] (current) – [Problems with different train and dev/test set dist] phreazer | ||
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| * Also see what examples algo got right (not only wrong) | * 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) | * 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% | ||
| + | |||
| + | ====== Data mismatch problems ====== | ||
| + | |||
| + | * Error analysis to understand difference between training and dev/test set | ||
| + | * Make training more similar / collect more data similar to dev/test set (e.g. simulate audio environment) | ||
| + | * Artificial data synthesis | ||
| + | * Problems: Possible that sampling from too few data (for human it might appear ok) | ||