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Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations.
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tree decision data trees information used set node classification displaystyle feature split gain features target value variable regression algorithms learning
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Decision tree learning | is a | supervised learning approach used in statistics | 0.90 | text |
| categorical sequences.Decision trees are among the most popular machine learning algorithms given their intelligibility | instance of | the concept of regression tree can be extended to any kind of object equipped with pairwise dissimilarities | 0.80 | text |
| simplicity because they produce algorithms that are easy to interpret | instance of | the concept of regression tree can be extended to any kind of object equipped with pairwise dissimilarities | 0.80 | text |
| visualize | instance of | the concept of regression tree can be extended to any kind of object equipped with pairwise dissimilarities | 0.80 | text |
| even for users without a statistical background.In decision analysis | instance of | the concept of regression tree can be extended to any kind of object equipped with pairwise dissimilarities | 0.80 | text |
| a decision tree can be used to visually | instance of | the concept of regression tree can be extended to any kind of object equipped with pairwise dissimilarities | 0.80 | text |
| explicitly represent decisions | instance of | the concept of regression tree can be extended to any kind of object equipped with pairwise dissimilarities | 0.80 | text |
| decision making | instance of | the concept of regression tree can be extended to any kind of object equipped with pairwise dissimilarities | 0.80 | text |
| information gain | instance of | and the process will continue for each impure node until the tree is complete.Compared to other metrics | 0.80 | text |
| the measure of | instance of | and the process will continue for each impure node until the tree is complete.Compared to other metrics | 0.80 | text |
| the greedy algorithm where locally optimal decisions are made at each node | instance of | practical decision-tree learning algorithms are based on heuristics | 0.80 | text |
| the dual information distance | instance of | some methods | 0.80 | text |
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