Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

Benchmark Model Rank Results
person-re-identification-on-esports-sensorsRandom Forest#1Accuracy: 52.1LogLoss: 0.01617ROC AUC: 0.919
person-re-identification-on-esports-sensorsLogistic Regression#2Accuracy: 48.8LogLoss: 0.01615ROC AUC: 0.884
person-re-identification-on-esports-sensorsSVM#3Accuracy: 45LogLoss: 0.01588ROC AUC: 0.89
person-re-identification-on-esports-sensorsKNN#4Accuracy: 41.5LogLoss: 0.05735ROC AUC: 0.84
person-re-identification-on-esports-sensorsRandom Guess#5Accuracy: 10LogLoss: 0.02303ROC AUC: 0.5
skills-evaluation-on-esports-sensors-datasetSVM#1Accuracy: 85.6LogLoss: 0.311ROC AUC: 0.945
skills-evaluation-on-esports-sensors-datasetLogistic Regression#2Accuracy: 83.8LogLoss: 0.596ROC AUC: 0.886
skills-evaluation-on-esports-sensors-datasetRandom Forest#3Accuracy: 80LogLoss: 0.456ROC AUC: 0.885
skills-evaluation-on-esports-sensors-datasetKNN#4Accuracy: 74.1LogLoss: 0.442ROC AUC: 0.899
skills-evaluation-on-esports-sensors-datasetRandom Guess#5Accuracy: 50LogLoss: 0.693ROC AUC: 0.5