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:
“How could a man be satisfied with a decision between such alternatives and under such circumstances? No more than he can be satisfied with his hat, which he’s chosen from among such shapes as the resources of the age offer him, wearing it at best with a resignation which is chiefly supported by comparison.”
—George Eliot [Mary Ann (or Marian)
“Behold I have given you every herb bearing seed which is upon the face of all the earth, and every tree, in which is the fruit of a tree yielding seed; to you it shall be for meat.”
—Bible: Hebrew Genesis 1:29.
But in a later context, God told the disgraced Adam, “and thou shalt eat the herb of the field” (Genesis 3:18)
“‘Tis very certain that each man carries in his eye the exact indication of his rank in the immense scale of men, and we are always learning to read it. A complete man should need no auxiliaries to his personal presence.”
—Ralph Waldo Emerson (1803–1882)