Decision Tree Splitting Explained | Entropy, Information Gain & Gini Index | ML

​ @csandai_learning @sonuyadav5504. 📞 Want 1:1 personal mentorship with me? Book a session on Topmate here: [https://topmate.io/sonuyadav5504] ---------------------------- 👉GATE DA most affordable course: https://gate.csandai.com just at 4999/- ---------------------------- In this video, we continue our Decision Tree Machine Learning series and understand how a Decision Tree decides the best split during training. We start with a quick revision of maximum depth, then learn what makes a split good or bad. After that, we understand purity, class mixing, entropy, information gain, Gini impurity, numerical feature splitting, categorical feature handling, and finally solve an end-to-end Decision Tree classification example using Study Hours and Attendance. This video is perfect for beginners who want to understand how Decision Tree actually learns from data before moving to coding, regression trees, pruning, Random Forest, and advanced ML algorithms. Topics covered: What is a good split in Decision Tree? Purity and class mixing explained Entropy in Decision Tree Information Gain explained simply Weighted entropy calculation Gini Impurity explained Entropy vs Gini Index When to use Gini and Entropy High cardinality categorical features Numerical feature split search Categorical feature handling End-to-end Decision Tree classification example How Decision Tree predicts for new data Watch till the end to clearly understand how Decision Tree training works step by step. 0:00 Revision of Maximum Depth 0:07 Maximum Depth Can Be n-1 0:41 How Decision Tree Splitting Works 0:50 What Makes a Good Split? 1:10 Meaning of Purity in Decision Tree 1:31 Pure vs Mixed Child Nodes 2:43 Prediction Confidence and Class Probability 3:25 How to Compare Split Quality 4:01 Purity Explained Simply 4:20 Classification Tree Reduces Class Mixing 4:36 Regression Tree Reduces Target Variation 4:52 Impurity Measures in Classification 5:17 Entropy and Information Gain 5:49 Entropy Formula Explained 6:15 Entropy Means Randomness 6:48 Information Gain Explained 7:12 Information Gain Formula 7:46 Why We Use Weighted Entropy 8:12 Pure Node vs Mixed Node Entropy 8:44 Conditional Entropy After Split 9:28 Weighted Sum of Child Entropy 11:10 Entropy Calculation Example 11:36 Full Information Gain Example 12:12 Left Child and Right Child Entropy 13:16 Final Information Gain Calculation 14:46 Gini Impurity Explained 15:06 Why Gini Is Used in CART Decision Tree 15:40 Gini Impurity Formula 16:33 Gini Calculation for Child Nodes 17:05 Entropy vs Gini Impurity 17:39 When to Use Gini or Entropy 18:17 Practical Advice for Industry ML Problems 18:32 High Cardinality Categorical Features 19:58 How Tree Searches Numerical Splits 20:27 Why Checking All Splits Can Be Expensive 21:05 How Modern Tree Libraries Optimize Splitting 21:29 How to Handle Categorical Features 21:48 Binary Split for Categorical Features 21:57 Multiway Split for Categories 22:29 One-Hot Encoding for Decision Tree 23:06 High Cardinality Warning 23:44 Practical Handling of Categorical Features 24:30 End-to-End Classification Example 24:39 Training Data: Study Hours and Attendance 25:12 Parent Node Entropy Calculation 25:36 Candidate Splits for Study Hours 26:36 Information Gain for Study Hours Split 27:45 Best Split Selection Using Attendance 28:16 Building the Decision Tree Step by Step 28:39 When Decision Tree Stops Splitting 29:05 Mixed Child Node and Further Splitting 30:06 Second Split Calculation 30:28 Final Decision Rule Using Study Hours 31:32 Final Learned Decision Tree 31:45 Prediction on New Sample 32:36 Important Learning from the Example 32:52 Next Video Preview: Regression Tree 32:59 Conclusion 🔗 Connect with Me: ---------------------------------- Instagram (YouTube) →   / sonuyadav_iitdelhi   Instagram (Personal) →   / sonuyadav5504   👉 Join WhatsApp Channel: https://whatsapp.com/channel/0029Vb7b... WhatsApp Group → https://chat.whatsapp.com/HjGuZZr07Uu... ---------------------------------- #DecisionTree #MachineLearning #Entropy #InformationGain #GiniIndex #DecisionTreeAlgorithm #MLHindi #MachineLearningHindi #DataScience #AIML #Classification #SupervisedLearning #DecisionTreeClassifier #AILabBySonu #ArtificialIntelligence #AIML #MachineLearning #DeepLearning #NLP #ComputerVision #GenerativeAI #LLM #AIforBeginners #TechEducation #FreeCourse #SonuYadav