This category contains 16 nodes.
Class for generating an alternating decision tree. The basic algorithm is based on: Freund, Y., Mason, L.: The alternating decision tree learning […]
Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the […]
Class for building and using a decision stump. Usually used in conjunction with a boosting algorithm. Does regression (based on mean-squared error) or […]
Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves. […]
Class for constructing an unpruned decision tree based on the ID3 algorithm. Can only deal with nominal attributes. No missing values allowed. Empty leaves […]
Class for generating a pruned or unpruned C4.5 decision tree. For more information, see Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan […]
Class for generating a grafted (pruned or unpruned) C4.5 decision tree. For more information, see Geoff Webb: Decision Tree Grafting From the […]
Class for generating a multi-class alternating decision tree using the LogitBoost strategy. For more info, see Geoffrey Holmes, Bernhard Pfahringer, […]
Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves. The algorithm can deal with […]
M5Base. Implements base routines for generating M5 Model trees and rules The original algorithm M5 was invented by R. Quinlan and Yong Wang made […]
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com, follow @NodePit on Twitter or botsin.space/@nodepit on Mastodon.
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.