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🤷 Quantifying Uncertainty — A Data Scientist’s Intro To Information Theory — Part 2/5: Entropy
Gain intuition into Entropy and master its applications in Machine Learning and Data Analysis. Python code provided. 🐍
My momma always said “Life was like a box of chocolates. You never know what you’re gonna get.”
— F. Gump (fictional philosopher and entrepreneur)
This is the second article in a series on information quantification — an essential framework for data scientists. Learning to measure information unlocks powerful tools for improving statistical analyses and refining decision criteria in machine learning.
In this article we focus on entropy — a fundamental concept that quantifies “on average, how surprising is an outcome?” As a measure of uncertainty, it bridges probability theory and real-world applications, offering insights into applications from data diversity to decision-making.
We’ll start with intuitive examples, like coin tosses and roles of dice 🎲, to build a solid foundation. From there, we’ll explore entropy’s diverse applications, such as evaluating decision tree splits and quantifying…
