User Intent Prediction in Information-seeking Conversations
Table 5. Experiment results for baseline classifiers
['Methods', 'Acc', 'Precision', 'Recall', 'F1']
Baseline Results Two ensemble methods, random forest and AdaBoost achieve the best overall performance of all baseline classifiers. AdaBoost achieves the best accuracy while random forest achieves the best F1 score. Surprisingly, ML-kNN performs relatively poorly despite its nature of an adapted algorithm for multi-label classification.