A Tutorial to Understand Decision Tree ID3 Learning Algorithm

Introduction Decision Tree learning is used to approximate discrete valued target functions, in which the learned function is approximated by Decision Tree. To imagine, think of decision tree as if or else rules where each if-else condition leads to certain answer at the end. You might have seen many online games which asks several question and lead... Continue Reading →

Entropy In Machine Learning

Entropy is a measure of randomness. In other words, its a measure of unpredictability. Let's take an example of a coin toss. Suppose we tossed a coin 4 times, and the output of the events came as {Head, Tail, Tail, Head}. Based solely on this observation, if you have to guess what will be the... Continue Reading →

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