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:
“Every decision is liberating, even if it leads to disaster. Otherwise, why do so many people walk upright and with open eyes into their misfortune?”
—Elias Canetti (b. 1905)
“A pinecone does not fall far from the tree trunk.”
—Estonian. Trans. by Ilse Lehiste (1993)
“Strange as it may seem, no amount of learning can cure stupidity, and formal education positively fortifies it.”
—Stephen Vizinczey (b. 1933)