Editing Machine learning (section)

=== Decision trees ===
{{Main|Decision tree learning}}
[[File:Decision Tree.jpg|thumb|A decision tree showing survival probability of passengers on the [[Titanic]]]]

Decision tree learning uses a [[decision tree]] as a [[Predictive modeling|predictive model]] to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modelling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, [[leaf node|leaves]] represent class labels, and branches represent [[Logical conjunction|conjunction]]s of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically [[real numbers]]) are called regression trees. 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 the resulting classification tree can be an input for decision-making.