Decision Tree Advantages
Amongst other data mining methods, decision trees have various advantages:
- Simple to understand and interpret. People are able to understand decision tree models after a brief explanation.
- Requires little data preparation. Other techniques often require data normalisation, dummy variables need to be created and blank values to be removed.
- Able to handle both numerical and categorical data. Other techniques are usually specialised in analysing datasets that have only one type of variable. Ex: relation rules can be used only with nominal variables while neural networks can be used only with numerical variables.
- Uses a white box model. If a given situation is observable in a model the explanation for the condition is easily explained by boolean logic. An example of a black box model is an artificial neural network since the explanation for the results is difficult to understand.
- Possible to validate a model using statistical tests. That makes it possible to account for the reliability of the model.
- Robust. Performs well even if its assumptions are somewhat violated by the true model from which the data were generated.
- Performs well with large data in a short time. Large amounts of data can be analysed using standard computing resources.
Read more about this topic: Decision Tree Learning
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—Friedrich Nietzsche (1844–1900)
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And men her golden entrails did espy,
This mischief then into the world was brought,
This framed the mint which coined our misery.
...
And thus began th’exordium of our woes,
The fatal dumb-show of our misery;
Here sprang the tree on which our mischief grows,
The dreary subject of world’s tragedy.”
—Michael Drayton (1563–1631)
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—Leontine Young (20th century)