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What is desirable? Hight information gain or High entropy

 In the context of decision trees and feature selection, it is desirable to have high Information Gain and low entropy.

Here's why:

  1. Information Gain (IG): Information Gain measures the reduction in entropy (or impurity) when a dataset is split based on a particular feature. The higher the Information Gain, the more information the feature provides in reducing uncertainty about the target variable. In other words, a high IG indicates that the feature is highly informative for making accurate predictions.


  2. Entropy: Entropy, on the other hand, represents the impurity or randomness in a dataset. When entropy is high, it means the data is more disordered and less informative for making predictions. In the context of decision trees, the goal is to minimize entropy, which translates to finding features that can split the data into subsets that are more homogenous with respect to the target variable.

So, in summary, you want high Information Gain because it signifies that the feature contributes significantly to reducing the uncertainty (entropy) in the dataset, making it easier for the decision tree or model to make accurate predictions.

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