The alternating decision tree structure consists of two components: decision nodes and prediction nodes. Decision nodes specify a predicate condition. Prediction nodes specify a value to add to the score based on the result of the decision node. Each decision node can be seen as a conjunction between a precondition (the decision node was reached) and the condition specified in the decision node.

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  • The alternating decision tree structure consists of two components: decision nodes and prediction nodes. Decision nodes specify a predicate condition. Prediction nodes specify a value to add to the score based on the result of the decision node. Each decision node can be seen as a conjunction between a precondition (the decision node was reached) and the condition specified in the decision node. Perhaps the easiest way to understand the interaction of decision and prediction nodes is through an example. The following example is taken from JBoost performing boosting for 6 iterations on the spambase dataset. Positive examples indicate that the message is spam and negative examples are not spam. During each iteration, a single node is added to the ADTree. The ADTree determined by the learning algorithm implemented in JBoost is: The tree construction algorithm is described below in the Description of the algorithm section. We now show how to interpret the tree once it has been constructed. We focus on one specific instance: For this instance, we obtain a score that determines the classification of the instance. This score not only acts as a classification, but also as a measure of confidence. The actual order that the ADTree nodes are evaluated will likely be different than the order in which they were created. That is, the node from iteration 4 can be evaluated before the node from iteration 1. There are constraints to this (e.g. node from iteration 2 must be evaluated before the node from iteration 5). In general, either breadth-first or depth-first evaluation will yield the correct interpretation. The following table shows how the score is created (progressive score) for our above example instance: There are a few observations that we should make The final classification of the example is positive (0.657), meaning that the example is considered to be spam. All nodes at depth 1 have their predicate evaluated and one of their prediction nodes contributes to the score. Thus a tree with depth 1 is the equivalent of boosted decision stumps. If a decision node is not reached (the node from iteration 5 in the above example) then the node's predicate and subsequent prediction nodes will not be evaluated.
  • Un Árbol de decisión alternativo es un método de clasificación proveniente del aprendizaje automático conocido en inglés como Alternating Decision Tree (ADTree). Las estructuras de datos y el algoritmo son una generalización de los árboles de decisión. El ADTree fue introducido por Yoav Freund y Llew Mason en 1999.
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  • The alternating decision tree structure consists of two components: decision nodes and prediction nodes. Decision nodes specify a predicate condition. Prediction nodes specify a value to add to the score based on the result of the decision node. Each decision node can be seen as a conjunction between a precondition (the decision node was reached) and the condition specified in the decision node.
  • Un Árbol de decisión alternativo es un método de clasificación proveniente del aprendizaje automático conocido en inglés como Alternating Decision Tree (ADTree). Las estructuras de datos y el algoritmo son una generalización de los árboles de decisión. El ADTree fue introducido por Yoav Freund y Llew Mason en 1999.
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  • Alternating decision tree
  • Árbol de decisión alternativo
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