Cross Entropy Calculator Online

Explanation

Cross-entropy is frequently employed as a loss function for classification issues, however historically speaking, the majority of cross-entropy explanations are based on communication theory, which data scientists might not be familiar with. Without understanding entropy first, as well as what information is, it is impossible to comprehend cross-entropy.

What is Entropy?

Entropy is a measure of the unpredictability or disorder of the data that machine learning algorithms are analyzing. In other words, entropy is the machine learning metric that assesses the system's impurity or unexpected/unpredictable nature.
Each piece of information that is processed by the system has a distinct value to make and can be utilised to build conclusions. In machine learning, entropy will be lower if it is simpler to draw a useful conclusion from a piece of information, and higher if it is more challenging to draw any conclusion at all.
In machine learning, entropy is a helpful tool for comprehending a variety of ideas, including feature selection, creating decision trees, fitting classification models, etc. You need to be well-versed on entropy in machine learning to be a professional data scientist and machine learning engineer.

What is Cross Entropy?

When two probability distributions are applied to the same collection of occurrences, cross-entropy calculates the relative entropy between them. It makes sense that you may compute the entropy for Q using probability weights from P in order to determine the cross-entropy between P and Q.

How to use Cross Entropy Calculator?

• You first need to enter the value of P - Probabilities in the first input field given in the form. Remember each probability is seperated with comma and each value must be in between 0 and 1.
• Then enter the value of Q - Probabilities in the second input field given in the form. Remember each probability is seperated with comma and each value must be in between 0 and 1.
• Click on the button submit. The result will automatically appear below.

More Calculation Examples (Cross Entropy Examples)

• Probabilities - P: 0.1, 0.5, 0.7, 0.4
• Probabilities - Q: 0.2, 0.3, 0.7, 0.1
• Cross Entropy = 2.79


• Probabilities - P: 0.6, 0.5, 0.5, 0.4
• Probabilities - Q: 0.2, 0.8, 0.9, 0.1
• Cross Entropy = 2.959


• Probabilities - P: 0.6, 0.7, 0.3, 0.4, 0.1
• Probabilities - Q: 0.3, 0.2, 0.8, 0.9, 0.1
• Cross Entropy = 3.157


• Probabilities - P: 0.6, 0.7, 0.3
• Probabilities - Q: 0.2, 0.8, 0.3
• Cross Entropy = 2.14