Entropy aims to measure the amount of in a system where, in context of classical information theory, the higher the entropy the more bits/information will be required to encode a message. Notes: Keep the idea of entropy in your back pocket as it will be applied to classification problems! We will be using entropy as a measure of uncertainty and as a way to compare the similarity of two probability distributions. strucutre uncertainty/surprise randomness O certainty

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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**Entropy aims to measure the amount of ____ in a system where, in the context of classical information theory, the higher the entropy the more bits/information will be required to encode a message.**

**Notes:** Keep the idea of entropy in your back pocket as it will be applied to classification problems! We will be using entropy as a measure of uncertainty and as a way to compare the similarity of two probability distributions.

- ○ structure
- ○ uncertainty/surprise
- ○ randomness
- ○ certainty

**Explanation:**

Entropy is a key concept in information theory used to quantify the uncertainty or unpredictability in a set of data or a signal. It's also fundamental when solving classification problems and plays a crucial role in decision-making algorithms. The higher the entropy, the more information is required to describe the state of the system. This makes it an essential tool for measuring how much surprise or randomness is present in a dataset.
Transcribed Image Text:**Entropy aims to measure the amount of ____ in a system where, in the context of classical information theory, the higher the entropy the more bits/information will be required to encode a message.** **Notes:** Keep the idea of entropy in your back pocket as it will be applied to classification problems! We will be using entropy as a measure of uncertainty and as a way to compare the similarity of two probability distributions. - ○ structure - ○ uncertainty/surprise - ○ randomness - ○ certainty **Explanation:** Entropy is a key concept in information theory used to quantify the uncertainty or unpredictability in a set of data or a signal. It's also fundamental when solving classification problems and plays a crucial role in decision-making algorithms. The higher the entropy, the more information is required to describe the state of the system. This makes it an essential tool for measuring how much surprise or randomness is present in a dataset.
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