Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain

Decision Trees use metrics like Entropy and Gini Impurity to make split decisions. Entropy measures the disorder or randomness in a dataset, while Gini Impurity quantifies the probability of misclassifying a randomly chosen element. Information Gain, derived from these metrics, guides the tree in selecting the most informative features for optimal data splits, contributing to effective decision-making in classification tasks. ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ⌚Time Stamps⌚ 00:00 - Intro 00:14 - Example 1 03:00 - Where is the Tree? 04:00 - Example 2 06:09 - What if we have numerical data? 07:57 - Geometric Intuition 10:50 - Pseudo Code 11:54 - Conclusion 14:00 - Terminology 14:53 - Unanswered Questions 16:16 - Advantages and Disadvantages 18:04 - CART 18:45 - Game Example 21:45 - How do decision trees work? / Entropy 22:15 - What is Entropy 25:40 - How to calculate Entropy 29:40 - Observations 31:35 - Entropy vs Probability 36:20 - Information Gain 41:40 - Gini Impurity 50:30 - Handling Numerical Data