How a decision tree works and how it extends to random forests?
Random forests are built using a method called bagging in which each decision trees are used as parallel estimators. If used for a classification problem, the result is based on majority vote of the results received from each decision tree. Random forest regression takes mean value of the results from decision trees.
What is J48 decision tree?
C4. 5 (J48) is an algorithm used to generate a decision tree developed by Ross Quinlan mentioned earlier. The decision trees generated by C4. 5 can be used for classification, and for this reason, C4. 5 is often referred to as a statistical classifier.
Which is a Weka specific reference to a tree?
RandomTree is a Weka specific reference to a single tree of a RandomForest. This equals random feature selection at each node in just one tree on the entire data set. Reduced-Error Pruning Decision Tree (REPTree) REPTree (Reduced-Error Pruning) is another algorithm that is specific to Weka.
What can you do with a Weka decision tree?
This you can do on different formats of data files like ARFF, CSV, C4.5, and JSON. Weka even allows you to add filters to your dataset through which you can normalize your data, standardize it, interchange features between nominal and numeric values, and what not!
How to create randomForest method in Weka-Dev?
RandomizableParallelIteratedSingleClassifierEnhancer Methods inherited from class weka.classifiers. ParallelIteratedSingleClassifierEnhancer Methods inherited from class weka.classifiers. IteratedSingleClassifierEnhancer Methods inherited from class weka.classifiers. SingleClassifierEnhancer Methods inherited from class weka.classifiers.
How are decision trees and random forests used?
If used for a classification problem, the result is based on majority vote of the results received from each decision tree. For regression, the prediction of a leaf node is the mean value of the target values in that leaf. Random forest regression takes mean value of the results from decision trees.