Person Re Identification On Esports Sensors

Metrics

Accuracy

LogLoss

ROC AUC

Results

Performance results of various models on this benchmark

				Paper Title
Random Forest	52.1	0.01617	0.919	Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
Logistic Regression	48.8	0.01615	0.884	Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
SVM	45	0.01588	0.89	Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
KNN	41.5	0.05735	0.84	Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
Random Guess	10	0.02303	0.5	Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

0 of 5 row(s) selected.

Person Re Identification On Esports Sensors

Metrics

Accuracy

LogLoss

ROC AUC

Results

Performance results of various models on this benchmark

				Paper Title
Random Forest	52.1	0.01617	0.919	Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
Logistic Regression	48.8	0.01615	0.884	Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
SVM	45	0.01588	0.89	Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
KNN	41.5	0.05735	0.84	Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
Random Guess	10	0.02303	0.5	Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

0 of 5 row(s) selected.

Person Re Identification On Esports Sensors | SOTA | HyperAI