Svm Decision Tree Comparison. The RBF SVM has very nice decision boundary. LR vs SVM.
Out that the SVM model can predict better than the KNN algorithm and Decision Tree. However they can definitely be powerful tools to solve regression problems yet many people miss this fact. For categorical independent variables decision trees are better than linear regression.
In addition to the conceptual part of Python coding this video will come across the hands on Comparison of KNN Decision Tree and SVM with Random ForestCurr.
The biggest difference between the two algorithms is that SVM uses the kernel trick to turn a linearly nonseparable problem into a linearly separable one unless of course we use the linear kernel while decision trees and forests based on them and boosted trees both to a lesser extent due to the nature of the ensemble algorithms split the input space into hyper-rectangles according to the target. The RBF SVM has very nice decision boundary. A decision tree can be used to visually and explicitly represent decisions and decision making. However they can definitely be powerful tools to solve regression problems yet many people miss this fact.