AdaBoost Vs Gradient Boosting: A Comparison Of Leading Boosting Algorithms

In recent years, ensemble learning or boosting has become one of the most promising approaches for analysing data in machine learning techniques. The method was initially proposed as ensemble methods based on the principle of generating multiple predictions and average voting among individual classifiers. Researchers from the Institute for Medical Biometry, Germany, have identified the key reasons for the success of statistical boosting algorithms as: (i) The ability of the boosting algorithms to incorporate automated variable selection and model choice in the fitting process,  (ii) The flexibility regarding the type of predictor effects that can be included in the final model and  (iii) The stability of these algorithms in high-dimensional data with several candidate variables rather than observations, a setting where most conventional estimation algorithms for regression settings collapse. Here, we have compared two of the popular boosting algorithms, Gradien