So if it is easier to draw a valuable conclusion from a piece of information, then entropy will be lower in Machine Learning, or if entropy is higher, then it will be difficult to draw any conclusion from that piece of information.Įntropy is a useful tool in machine learning to understand various concepts such as feature selection, building decision trees, and fitting classification models, etc. When information is processed in the system, then every piece of information has a specific value to make and can be used to draw conclusions from it. Further, in other words, we can say that entropy is the machine learning metric that measures the unpredictability or impurity in the system. Introduction to Entropy in Machine LearningĮntropy is defined as the randomness or measuring the disorder of the information being processed in Machine Learning. So let's start with a quick introduction to the entropy in Machine Learning. In this article, we will discuss what entropy is in Machine Learning and why entropy is needed in Machine Learning. The base of entropy comes from physics, where it is defined as the measurement of disorder, randomness, unpredictability, or impurity in the system. Almost everyone must have heard the Entropy word once during their school or college days in physics and chemistry. Machine Learning contains lots of algorithms and concepts that solve complex problems easily, and one of them is entropy in Machine Learning. Machine Learning is also the most popular technology in the computer science world that enables the computer to learn automatically from past experiences.Īlso, Machine Learning is so much demanded in the IT world that most companies want highly skilled machine learning engineers and data scientists for their business. We are living in a technology world, and somewhere everything is related to technology. Next → ← prev Entropy in Machine Learning
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