Contents
What is SVM and random forest?
Random Forest is intrinsically suited for multiclass problems, while SVM is intrinsically two-class. For multiclass problem you will need to reduce it into multiple binary classification problems. Random Forest works well with a mixture of numerical and categorical features.
Can decision trees capture boundaries?
Since decision trees make axis parallel boundaries they are sensitive to data rotation. The robustness of decision trees can be enhanced by using lots of them together and averaging their predicted value and use that as the final prediction. Such a classifier is called Random Forest.
Which is better for classification SVM or random forest?
For a classification problem Random Forest gives you probability of belonging to class. SVM gives you distance to the boundary, you still need to convert it to probability somehow if you need probability. For those problems, where SVM applies, it generally performs better than Random Forest.
Which is better a decision tree or a SVM?
To guide cost-complexity pruning, the misclassification rate is preferable. However, a simple model like Decision Tree tends to not have good predicting power. More complicated methods, such as random forests and boosting, usually yield better results, though there is a trade-off between interpretability and prediction accuracy.
How are random forests used in machine learning?
Random forests involve building many decision trees on a bootstrapped training set. When building decision trees, a random selection of m predictors among the full set of p predictors is chosen in each considered split. The split can only use one of those m selected predictors.
How is SVM used to find nonlinear boundaries?
The idea of SVM is to find nonlinear boundaries by constructing a linear boundary in a large, transformed version of the feature space. Simply, you have 2 categories in an x-y plane with points overlapping each other, and you can’t find a straight line that can perfectly separate them.