From: Identifying tweets of personal health experience through word embedding and LSTM neural network
Classifier | Accuracy | Precision (PET) | Recall (PET) | F1 (PET) | ROC/AUC |
---|---|---|---|---|---|
Logistic Regression | 2.52āĆā10āā08 | 1.85āĆā10ā09 | 5.48āĆā10āā09 | 5.87āĆā10ā10 | 1.46āĆā10āā09 |
Decision Tree | 1.80āĆā10āā04 | 1.51āĆā10āā04 | 6.99āĆā10āā06 | 1.92āĆā10āā06 | 1.16āĆā10āā05 |
KNN | 8.08āĆā10āā05 | 6.22āĆā10āā05 | 1.40āĆā10 āā03 | 8.50āĆā10āā05 | 1.29āĆā10 āā04 |
SVM | 1.17āĆā10āā08 | 4.61āĆā10āā08 | 1.74āĆā10āā04 | 7.89āĆā10āā07 | 5.02āĆā10āā06 |
BoW + Logistic Regr. | 4.26āĆā10 āā04 | 2.22āĆā10 āā04 | 1.79āĆā10āā04 | 9.85āĆā10 āā05 | 2.12āĆā10āā05 |