Decision tree learning: Difference between revisions

Revision as of 11:47, 15 September 2023

Revision as of 03:17, 9 November 2023

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* '''Able to handle both numerical and [[Categorical variable|categorical]] data.'''<ref name=":0" /> Other techniques are usually specialized in analyzing datasets that have only one type of variable. (For example, relation rules can be used only with nominal variables while neural networks can be used only with numerical variables or categoricals converted to 0-1 values.) Early decision trees were only capable of handling categorical variables, but more recent versions, such as C4.5, do not have this limitation.<ref name="tdidt" />

* '''Requires little data preparation.''' Other techniques often require data normalization. Since trees can handle qualitative predictors, there is no need to create [[dummy variable (statistics)|dummy variables]].<ref name=":0" />

* '''Uses a [[white box (software engineering)|white box]] or open-box<ref name="tdidt" /> model.''' If a given situation is observable in a model the explanation for the condition is easily explained by boolean[[Boolean logic]]. By contrast, in a [[black box]] model, the explanation for the results is typically difficult to understand, for example with an [[artificial neural network]].

* '''Possible to validate a model using statistical tests.''' That makes it possible to account for the reliability of the model.

* Non-parametric approach that makes no assumptions of the training data or prediction residuals; e.g., no distributional, independence, or constant variance assumptions