Table 1. Each model used in the gender prediction task was fine-tuned.

Optimal values for each parameter are marked in bold.

Model	Parameters
LogisticRegression	C:{1, .2, .5, 1, 2, 5, 10}
RandomForestClassifier	max_features:{all, sqrt, log2}, n_estimators:{1000}
GradientBoostingClassifier	max_features:{all, sqrt, log2}, learning_rate:{0.001, 0.002, 0.005, .01, .02, .05}, n_estimators:{1000}
AdaBoostClassifier	learning_rate:{0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1.0}, n_estimators:{1000}
SVC	C:{.01, .05, 0.1, .5, 1, 5, 10}, kernel:{linear, poly, rbf, sigmoid}