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Decision tree algorithm in ML

1. PRESENTED BY: 1. Dhavala V D M Adithya Naidu (RA2311026010448) 2. D Sai Karthik (RA2311026010460) 3. M Guru Karthik Reddy (RA2311026010457) 4. SG Jathin (RA2311026010473) DECISION TREE ALGORITHM

2. #1 What is a Decision Tree? #2 How the Algorithm Works #3 Real-World Applications #4 Strengths & Limitations #5 Conclusion & Key Takeaways TOPIC OUTLINE Today's Discussion

3. INTRODUCTION DIVE INTO DECISION TREES We're here today to explore one of the most powerful and intuitive algorithms in machine learning, prized for its clarity and predictive ability.

4. WHAT IS A DECISION TREE? A Decision Tree is a supervised machine learning algorithm that excels at modeling decision processes The algorithm works by creating a flowchart-like structure that sequentially splits data based on simple rules. This highly intuitive method makes it easy to visualize how predictions are made, and it can be used effectively for both Classification (predicting categories) and Regression (predicting numerical values).

5. HOW IT WORKS: STEP 1 - SPLITTING

6. HOW IT WORKS STEP 1 - SPLITTING The Decision Tree finds the single best feature to ask about, which instantly begins dividing the data into the purest possible groups. 1. Goal: Divide the dataset into smaller, homogeneous groups. 2. Mechanism: The algorithm uses metrics (Gini Impurity or Information Gain) to find the single optimal split point. 3. Process: Data follows one branch (e.g., Weather is Sunny) or another (e.g., Weather is Not Sunny).

7. HOW IT WORKS: DECISION NODES & LEAVES

8. HOW IT WORKS DECISION NODES & LEAVES Decision Nodes • The process continues branching recursively. • Each node acts as a feature test (the "question") that maximizes class separation. Leaf Nodes • These are the end points of the tree (terminal nodes). • They contain the final prediction— either the predicted class or a numerical value.

9. REAL-WORLD IMPACT

10. REAL-WORLD IMPACT REAL-WORLD IMPACT Now that we know how the Decision Tree works, let's explore where it creates the most real-world impact. 1. Medical Diagnosis: Disease Prediction by classifying patient data. 2. Banking & Finance: Loan Approvals and Credit Scoring. 3. Marketing: Customer Segmentation and Sentiment Analysis. 4. Cybersecurity: Fraud Detection and malicious activity classification.

11. ALGORITHM STRENGTHS

12. ALGORITHM STRENGTHS SIMPLICITY & EFFICIENCY The Decision Tree is a 'white-box' model—easy to audit and explain, which is crucial for decision-making. 1. Interpretability: They are easy to understand and visualize, providing a clear path from input to prediction. 2. Data Versatility: Handles both categorical and numerical data simultaneously. 3. Low Preprocessing: Requires little data cleaning or normalization compared to complex statistical models.

13. ALGORITHM LIMITATIONS

14. ALGORITHM LIMITATIONS CHALLENGES & SOLUTIONS The core challenge is overfitting—the tree gets too specific to the training data, failing in the real world 1. Overfitting Risk: Can grow too deep, essentially memorizing the training data. 2. Instability (High Variance): Sensitive to small data changes, which can lead to a completely different tree structure. 3. Solution: These issues are primarily solved by using Pruning or aggregating them into Ensemble Algorithms like Random Forests.

15. CONCLUSION Review #2 Core Value: It is highly Transparent and Easy to Interpret, making it a perfect "White-Box" model. Review #1 Simple yet Powerful: The Decision Tree is an excellent model for quick decisions and pattern discovery. Review #4 Foundational: It serves as the essential building block for advanced models like Random Forests (Ensemble Algorithms). Review #3 Mechanism: Uses sequential splits based on metrics like Gini Impurity to achieve purity in its nodes.

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