ADTree
Package
weka.classifiers.trees
Synopsis
Class for generating an alternating decision tree. The basic algorithm is based on:
Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999.
This version currently only supports two-class problems. The number of boosting iterations needs to be manually tuned to suit the dataset and the desired complexity/accuracy tradeoff. Induction of the trees has been optimized, and heuristic search methods have been introduced to speed learning.
Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (alternatingDecisionTrees)
Options
The table below describes the options available for ADTree.
Option |
Description |
|---|---|
debug |
If set to true, classifier may output additional info to the console. |
numOfBoostingIterations |
Sets the number of boosting iterations to perform. You will need to manually tune this parameter to suit the dataset and the desired complexity/accuracy tradeoff. More boosting iterations will result in larger (potentially more accurate) trees, but will make learning slower. Each iteration will add 3 nodes (1 split + 2 prediction) to the tree unless merging occurs. |
randomSeed |
Sets the random seed to use for a random search. |
saveInstanceData |
Sets whether the tree is to save instance data - the model will take up more memory if it does. If enabled you will be able to visualize the instances at the prediction nodes when visualizing the tree. |
searchPath |
Sets the type of search to perform when building the tree. The default option (Expand all paths) will do an exhaustive search. The other search methods are heuristic, so they are not guaranteed to find an optimal solution but they are much faster. Expand the heaviest path: searches the path with the most heavily weighted instances. Expand the best z-pure path: searches the path determined by the best z-pure estimate. Expand a random path: the fastest method, simply searches down a single random path on each iteration. |
Capabilities
The table below describes the capabilites of ADTree.
Capability |
Supported |
|---|---|
Class |
Binary class, Missing class values |
Attributes |
Binary attributes, Unary attributes, Date attributes, Missing values, Numeric attributes, Nominal attributes, Empty nominal attributes |
Min # of instances |
1 |