What Is Gini Index?
Table Of Contents:
- Introduction To Gini Index.
- Formula & Calculation.
- Gini Index In Decision Tree.
- Properties Of Gini Index.
- Weighted Gini Index.
- Gini Index Vs Entropy.
- Splitting Criteria and Gini Index.
- Advantages and Limitation Of Gini Index.
- Practical Implementation.
- Real-World Examples
- Advanced Topics
(1) Introduction To Gini Index.
Definition Of Gini Index
- In the context of decision trees, the Gini Index is a metric used to evaluate the purity of a split or node.
- It quantifies the probability of incorrectly classifying a randomly chosen element from the dataset if it were labeled randomly according to the class distribution.
Range Of Gini Index
Formula For Gini Index:
Example Of Gini Index:
After Splitting The Dataset Calculate The Gini Index:
(2) How Gini Index Used In Decision Trees ?
- The Gini Index is a critical component of decision trees, particularly in the CART (Classification and Regression Trees) algorithm.
- It is used to determine the “impurity” or “purity” of a node and helps in selecting the best feature to split the data at each step.
- In a decision tree, we aim to split the data in such a way that the resulting nodes (subsets) are as pure as possible (i.e., most of the data in the node belongs to a single class).
(3) Key Differences Between Gini Index & Entropy.
(3) Advantages and Disadvantages Of Gini Index
Advantages:
Limitation:
