Research map / Label-efficient and robust learning / Recognition and long-tail
Precision-Recall versus Accuracy and the Role of Large Data Sets
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| train on large instead of small training set (no correction) | K-NN · abalone | precision: 0.2500±0.0000 -> 0.5263±0.0000 | Helped |
| apply random undersampling on small training set | K-NN · abalone | precision: 0.2500±0.0000 -> 0.2000±0.0127 | Hurt |
| train on large instead of small training set (no correction) | K-NN · letter | precision: 0.9500±0.0000 -> 0.9870±0.0000 | Helped |
| apply random undersampling on small training set | K-NN · letter | precision: 0.9500±0.0000 -> 0.2620±0.0305 | Hurt |
| train on large instead of small training set (no correction) | K-NN · satimage | precision: 0.6707±0.0000 -> 0.7513±0.0000 | Helped |
| apply random undersampling on small training set | K-NN · satimage | precision: 0.6707±0.0000 -> 0.2950±0.0356 | Hurt |
| train on large instead of small training set (no correction) | K-NN · drug discovery | precision: 0.4825±0.0000 -> 0.9474±0.0000 | Helped |
| apply random undersampling on small training set | K-NN · drug discovery | precision: 0.4825±0.0000 -> 0.0017±0.0002 | Hurt |
| apply SMOTE+SVM/SMOTE+ENN resampling | Random Forest · UCI + drug discovery | precision and recall | Mixed |
| apply imbalance-correction methods with growing training size | K-NN · letter, drug discovery | precision and recall | No clear effect |
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