Support Vector Machines (SVM) and Random Forests (RF) have
consistently outperformed other machine learning algorithms on a variety of
problems. SVM can be used for classification and regression on many types of
data (e.g. nonlinear, high dimensional), but cannot handle missing or mixed data.
This research implements a permutation-based variable...
Support Vector Machines (SVM) and Random Forests (RF) have
consistently outperformed other machine learning algorithms on a variety of
problems. SVM can be used for classification and regression on many types of
data (e.g. nonlinear, high dimensional), but cannot handle missing or mixed data.
This research implements a permutation-based variable...
