• Facets (new session)
• Description
• Metadata
• Settings
  • Rule: asEquivalentb3sb3sifpdbprdf-labelfacetshttp://dbpedia.org/resource/inference/rules/dbpedia#http://dbpedia.org/resource/inference/rules/opencyc#http://dbpedia.org/resource/inference/rules/umbel#http://dbpedia.org/resource/inference/rules/yago#http://dbpedia.org/schema/property_rules#http://localhost:8890/dataspacehttp://www.ontologyportal.org/inference/rules/SUMO#http://www.ontologyportal.org/inference/rules/WordNet#http://www.w3.org/2002/07/owl#ldpoplwebskos-transvirtrdf-labelvirtrdf-urlNone
    
  • Inverse Functional Properties:
  • "Same As": Disabled (fastest) Apply to subjects, objects and predicates (not recommended on big datasets)

In computer science, a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic regression (LR) and decision tree learning. Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models at its leaves to provide a piecewise linear regression model (where ordinary decision trees with constants at their leaves would produce a piecewise constant model). In the logistic variant, the LogitBoost algorithm is used to produce an LR model at every node in the tree; the node is then split using the C4.5 criterion. Each LogitBoost invocation is warm-started from its results in the parent node. Finally, the tree is pruned.