What is entropy in the context of Machine Learning?
Understand the Problem
The question is asking about the definition of entropy and its role within the field of Machine Learning, specifically which option correctly describes it.
Answer
Entropy measures the disorder or uncertainty in a dataset in machine learning.
Entropy in machine learning is a measure of the level of disorder or uncertainty in a dataset. It quantifies the amount of information in the data and is used to evaluate the quality of models, such as determining splits in decision trees.
Answer for screen readers
Entropy in machine learning is a measure of the level of disorder or uncertainty in a dataset. It quantifies the amount of information in the data and is used to evaluate the quality of models, such as determining splits in decision trees.
More Information
Entropy is crucial in decision trees where it's used for selecting the best split by assessing the homogeneity of a dataset. A lower entropy indicates less unpredictability in the dataset.
Tips
A common mistake is confusing entropy with information gain. While entropy measures disorder, information gain is used for deciding data splits and requires understanding the difference between them.
Sources
- Entropy in Machine Learning - Javatpoint - javatpoint.com
- What is entropy in machine learning - Magic Slides - magicslides.app
- Entropy in Machine Learning - Deepgram - deepgram.com
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