Understanding Entropy made me a better data scientist
I remember several years ago when I was reshaping my career from finance into data science and being fascinated about how the book Data Science for Business (Provost & Fawcett) introduced the concept of Entropy in their classification examples, so elegantly, so powerful yet so simple. What they were explaining was nothing new to me, I had learned about machine learning and data science way before reading that book, yet that specific approach changed my whole interpretation of the subject. I always thought it was something truly beautiful to write about, hence this small article! Let’s do it!
It quantifies data uncertainty, helping understanding how much additional information is required for more accurate predictions.
It guides feature selection in machine learning, determining the information gain of each feature and thus their importance.
It is fundamental in decision tree algorithms, shaping the tree structure by prioritizing the most informative features.
It measures dataset impurity, reflecting the degree of mixedness of data classes.
By assisting effective feature selection, it helps prevent overfitting, leading to more robust and effective models.
If you’re more of a visual person and would prefer to watch some YouTube videos, I’d be happy to refer you to two of my favorite about Entropy, each one of them from a very different angle:
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