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 <a href="https://www.amazon.es/Data-Science-Business-Data-Analytic-Thinking-ebook/dp/B00E6EQ3X4/ref=sr_1_1?__mk_es_ES=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=3T07GVD9ETC2W&keywords=data+science+for+business&qid=1688363602&sprefix=data+science+for+business%2Caps%2C93&sr=8-1" rel="noopener ugc nofollow" target="_blank">Data Science for Business (Provost & Fawcett)</a> 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! Entropy is a crucial concept in data science. <ul> <li>It quantifies data uncertainty, helping understanding how much additional information is required for more accurate predictions.</li> <li>It guides feature selection in machine learning, determining the information gain of each feature and thus their importance.</li> <li>It is fundamental in decision tree algorithms, shaping the tree structure by prioritizing the most informative features.</li> <li>It measures dataset impurity, reflecting the degree of mixedness of data classes.</li> <li>By assisting effective feature selection, it helps prevent overfitting, leading to more robust and effective models.</li> </ul> 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: <a href="https://medium.com/@gabrielpierobon/understanding-entropy-made-me-a-better-data-scientist-3196a3ff6ab4">Website</a>