I’m trying to wrap my head around entropy. I remember using it a bit in my machine learning class, but the formulas always confused me. I’m working on a project now where I need to calculate the entropy of a dataset, and I’m feeling a bit lost. Can someone explain how to calculate entropy for a dataset? I’m not looking for the super technical stuff, just a basic understanding and maybe a simple example.
I remember diving into entropy during my machine learning class and feeling overwhelmed by the formulas too. In essence, entropy measures the uncertainty or randomness in a dataset. To calculate it, you can follow these basic steps: First, determine the probability of each class in your dataset. Then, use the formula for entropy: −∑(pi⋅log2(pi))-\sum (p_i \cdot \log_2(p_i))−∑(pi⋅log2(pi)), where pip_ipi is the probability of each class. For example, if you have a dataset with two classes, A and B, with probabilities 0.6 and 0.4 respectively, the entropy would be −[0.6⋅log2(0.6)+0.4⋅log2(0.4)]-[0.6 \cdot \log_2(0.6) + 0.4 \cdot \log_2(0.4)]−[0.6⋅log2(0.6)+0.4⋅log2(0.4)]. This gives you a measure of how mixed or pure your dataset is, helping guide decisions in your project.
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Hey @ethan Entropy is like measuring how mixed up or unpredictable your data is. Imagine you have a bag of candy with 5 red and 5 blue pieces. If you randomly pick one, you don’t know what you’ll get—it’s kinda unpredictable, right? That’s high entropy. But if you had 10 red pieces, it’s super predictable—low entropy.
To calculate it, you use a formula (sounds scary but it’s not too bad). You just take each type of candy, figure out how likely you are to pick it (probability), and then do some quick math to add up all those “surprise” levels. For example, if it’s 50/50, the entropy is higher than if it’s, say, 90/10.
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