A quick post on Decision Trees

Today is my one year anniversary studying at Bentley University in MA, US. And this short post on this special day is devoted to Decision Trees, a simple but often misused method. During my short time here, and limited times talking to marketing analysts, I saw some cases when they just take the rules directly from decision trees and throw them in decks and say that they will show that to executives as "insights". I think they should be a bit more careful since the decision tree can be rather unstable, i.e. small changes in the dataset can result in completely different trees.

Below are some python scripts, which i used to build classification trees on the iris data. But each time, I do some sort of random sampling and feed the sample to the algorithm to build trees. You can look at the difference  yourself.

I will just upload pictures of a few of these trees.

iris_dt_entropy_70_percent_train_seed_36 iris_dt_entropy_70_percent_train_seed_1106

The two trees above are built on different random samples of the iris dataset. From the first one you can get the rule: if petal length is less than or equal to 2.45 cm then the flower is a setosa. Wheras from the second decision tree you get the rule: if petal width is less than or equal to 0.8 cm then the flower is a setosa. Take a quick look at the scatter plot you will see that they are both pretty good rules, and if you only build one decision tree, you will miss one of the very good rules.


Below are two trees built on different samples of the iris dataset again. And I personly would not try to interpret the splitting rules from the third level in the tree and on (take root node as the first level).

iris_dt_entropy_num_per_class_15_45_5 iris_dt_entropy_num_per_class_15_45_10

Anyway, the takeaway is that: try different algorithms, try different samples as input, build many trees and take what's common from them, rather than simply build one tree.

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