Fig. 6
From: Attentional network deficits in patients with migraine: behavioral and electrophysiological evidence
Classification and regression models. (A) Feature selection for developing gradient boosting (XGB) classification model to distinguish patients from healthy controls and regression model to predict clinical characteristics of patients; (B) Importance ranking for each feature in the XGB classification model; (C) Correlation between real headache frequency and predicted headache frequency estimated by the XGB regression model using leave-one-out cross-validation; (D) Average reduction in loss for each feature across all splits and trees in regression model for predicting headache frequency. *p < 0.05. PSS-14, Perceived Stress Scale-14; IES, inverse efficiency score; RT, reaction time; IIRTV, intra-individual reaction time variability; ERS, event-related synchronization; AV, arousal vigilance; amp, amplitude; PSD, power spectral density; lat, latency; EV, executive vigilance; EC, executive control