Class for generating an alternating decision tree. The basic algorithm is based on: Freund, Y., Mason, L.: The alternating decisi…

Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. F…

Class for constructing a tree that considers K randomly chosen attributes at each node. Performs no pruning. Also has an option t…

Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error p…

Class implementing minimal cost-complexity pruning. Note when dealing with missing values, use "fractional instances" method inst…

Interactively classify through visual means. You are Presented with a scatter graph of the data against two user selectable attri…

Class for generating a multi-class alternating decision tree using the LogitBoost strategy. For more info, see Geoffrey Holmes, B…

Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves. …

M5Base. Implements base routines for generating M5 Model trees and rules The original algorithm M5 was invented by R. Quinlan and…

Class for generating a decision tree with naive Bayes classifiers at the leaves. For more information, see Ron Kohavi: Scaling Up…

Class for constructing a random forest*. (based on WEKA 3.6) For further options, click the 'More' - button in the dialog. All we…

Class for building and using a decision stump. Usually used in conjunction with a boosting algorithm. Does regression (based on m…

Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the i…

Class for constructing an unpruned decision tree based on the ID3 algorithm. Can only deal with nominal attributes. No missing va…

Class for generating a pruned or unpruned C4.5 decision tree. For more information, see Ross Quinlan (1993). C4.5: Programs for M…

Class for generating a grafted (pruned or unpruned) C4.5 decision tree. For more information, see Geoff Webb: Decision Tree Graft…

A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive dat…

Class for constructing an unpruned decision tree based on the ID3 algorithm Can only deal with nominal attributes.No missing valu…

Implements the isolation forest method for anomaly detection The data is expected to have two class values for the class attribut…

Class for generating a pruned or unpruned C4.5 decision tree For more information, see Ross Quinlan (1993).C4.5: Programs for Mac…