| Popis: |
Over the past two decades machine learning has become one of the mainstays of information technology and with that, a rather central, albeit usually hidden, part of our life. With the ever increasing amounts of data becoming available there is good reason to believe that smart data analysis will become even more pervasive as a necessary ingredient for technological progress. This chapter presents the idea of utilizing Brooks–Iyengar algorithm to improve the random forest classifiers applicable to various classification tasks. The proposed method is also applicable to regression tasks as the dependent variable of regression can be modeled as a multi-valued label using the technique of discretization. The extension of classifiers does not usually result in a regressor as precise as its corresponding classifier since the discretization error of dependent variable adds up across different trees, creating a less precise forest regressor comparing with the original forest classifier. In this chapter, we show how to mitigate this issue using Brooks–Iyengar fusion algorithm. |
