Do not require a large number of initially specified parameters

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Decision trees are capable of independently generating rules in areas that are unfamiliar to the specialist.
They are easy to visualize, which allows you to perceive not only the model as a whole, but also to predict the outcome for individual entities in the tree.

Capable of working with categorical and numeric identifiers.
Allows you to quickly solve a problem thanks to high-quality prediction of the result.
But this method has not only advantages, but also disadvantages, which must finland mobile database also be taken into account when working with it:

There is a possibility of errors in object classification tasks. This is due to the large number of classes with a small number of training examples.