Can you explain to me the parameter gamma in XGBoost algorithm? What is its impact on the model's perfomance?
Gamma controls how much weight should be given to mistakes when learning. Lower values give more importance to rare events and higher ones to common ones, as measured by their likelihood ratio statistics dL/dgamma with respect to a reference distribution of parameters (usually gammas = 1).