To develop a generalizable model that avoids overfitting, we perform feature selection using statistical tests and all relevant ensemble learning models. It adds randomness to the data by creating shuffled copies of all features (shadow feature), and then trains Random Forest classifier on the extended data. 
 Question: What model is used to achieve 5% improvement on F1 for identifying depressed individuals on Twitter?
Random Forest classifier