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Can anyone explain the entropy in Machine Learning?

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In Machine Learning, Entropy is a measure to calculate the impurity of the group. Entropy is used in tree algorithms such as Decision tree to decide where to split the data. Entropy helps to check the homogeneity of the data. Entropy is calculated using the following formula:

Entropy = ∑ -pi log2 (pi)

where pi is the probability of ith class

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