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Gini Index In Decision Tree.
GINI Index In Decision Tree Table Of Contents: What Is the GINI Index? Interpreting GINI Index. Example Of GINI Index. What Is the GINI Coefficient? (1) What Is GINI Index? The Gini index, also known as the Gini impurity, is a measure used in decision tree algorithms to quantify the impurity or randomness of a set of examples within a particular node. It is an alternative criterion, alongside entropy, for determining the best feature to split the data. The Gini index is calculated based on the distribution of class labels within a node. It measures the probability of incorrectly classifying
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Information Gain In Decision Tree.
Information Gain Table Of Contents: What Is Information Gain? Example Of Information Gain. (1) What Is Information Gain? Information gain is a measure used in decision tree algorithms to determine the best feature to split the data. It quantifies how much information a particular feature contributes to reducing the entropy or impurity within a node. The information gain is calculated by comparing the entropy of the parent node (before the split) with the weighted average of the entropies of the child nodes (after the split), considering each possible outcome of the feature being evaluated. A higher information gain indicates that
