Decision tree learning, used in statistics, data mining and machine learning, uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. More descriptive names for such tree models are classification trees or regression trees. In these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels.
In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data but not decisions; rather the resulting classification tree can be an input for decision making. This page deals with decision trees in data mining.
Read more about Decision Tree Learning: General, Types, Formulae, Decision Tree Advantages, Limitations
Famous quotes containing the words decision, tree and/or learning:
“The decision to have a child is both a private and a public decision, for children are our collective future.”
—Sylvia Ann Hewitt (20th century)
“Strong to break dead things,
the young tree, drained of sap,
the old tree, ready to drop,
to lift from the rotting bed
of leaves, the old
crumbling pine tree stock.”
—Hilda Doolittle (1886–1961)
“You taught me language, and my profit on’t
Is, I know how to curse. The red plague rid you
For learning me your language!”
—William Shakespeare (1564–1616)