Fig. 2
From: Supervised learning for infection risk inference using pathology data
High level diagram of the work-flow followed to build the models and obtain the results presented in this paper. First, data cleaning and outlier removal is performed. The remaining observations are grouped as complete or incomplete profiles. The former is further split into Cross-Validation Set (CVS) and Hold-out Set (HOS). Ten-Fold Stratified Cross-Validation is performed on CVS and two outputs are obtained in this step: a preprocessing equation to transform new observations (T) and a calibrated model (M) which are later used. It is important to highlight that sampling and preprocessing are performed using the training set while calibration is achieved from completely unseen observations. The performance of calibrated models is evaluated in HOS and \(\{F_{n}\}_{n=1}^{5}\)