Readability of Decision Trees

One of the most often cited advantage of decision trees is their readability. Several data miners (to whom I belong) justify the use of this technique since it is quite easy to understand the obtained model (no black box). However, there are certain issues that make decision trees unreadable.

First, there is normalization (or standardization). In most projects, data have to be normalized before using decision tree. Therefore, once you plot the tr…

One of the most often cited advantage of decision trees is their readability. Several data miners (to whom I belong) justify the use of this technique since it is quite easy to understand the obtained model (no black box). However, there are certain issues that make decision trees unreadable.

First, there is normalization (or standardization). In most projects, data have to be normalized before using decision tree. Therefore, once you plot the tree, values are meaningless. Of course, you can map the data back in the original format, but it has to be done.

Second is the number of trees. In the project I carry on at my job, I can have 100 or more decision trees by month (see this post for more details). It is clearly impossible to read all these trees even if they are independently understandable. The same happens with random forests. When there are 1000 trees voting for a given class, how can one understand the process (or rules) that produce the class output?

Decision trees still have a lot of advantages. However, the “readability” advantage must be taken with care. It may be valid in some applications, but can often be a mirage.

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