Decision Trees, entropy and information gain

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So you need to decide on something and wish to perform it the machine-learning way? you have come to the right www page. decision trees are basically a bunch of questions you ask yourself (or the machine if you are a software developer) until you get to an prediction of what the answer is most likely to be according to the decision tree you or your machine have built. Examples:

  • Should we hire that candidate?
  • Should I trust this Guy?
  • Is the world coming to an end?

Let’s take the third example as the most frequent question you might ask yourself.


Is the world comming to an end?

|   some-have-atom-bombs: Y
|   |   with-dictator-leader: Y
|   |   |    the-end-of-the-world
|   |   no-dictator-leader: N
|   |   |    not-the-end-of-the-world

What you see is a tree we built, the tree is not nicely built from actual-data, usually we build it based on raw data we gather from the past collected data, this time, we just imagined-simulated a few worlds in some imagined-world-simulation game and saw the end results and from it decided on the decision-tree. But how should you decide on a decision-tree?

You first need to decide on the first question. For the first question you first need to decide which one will split the data to the most purest results. By this we mean we examine all features we have (have-atom-bombs, with-dictator-leader, cows-make-meow-instead-of-moo). Now with all these features in hand we examine for each and every of them (n features), determine which split yields the purest outcome. If you take this question then two different answers (binary tree) would claim the most purest (least enthropical ones) results - the best choice would be one who splits the data to two separate groups (comming-to-end and not-comming-to-end each in its own group - pure). After you have chosen the first feature you then choose the next one among n-1 features, then the next feature-question among n-2 etc. You do this by calculating the entropy which measures the impurity for each feature result, you calculate it before and after the new rule or decision-question for each and every of the reminder features, the one which yields the highest entropy reduction is the one you choose for the next decision-question

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