This document discusses decision trees, a type of classification model. It defines classification as the process of dividing datasets into categories or groups by adding labels. Decision trees are graphical representations that show all possible solutions to decisions based on conditions. The document outlines types of classification models, provides examples of decision trees, and defines key terminology like branch nodes and leaf nodes. It also discusses common algorithms like CART that are used to build decision trees.

1. Decision Tree
Dr. Varun Kumar
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2. Outlines
1 What is classification ?
2 Types of classification
3 Introduction to Decision Tree
4 Terminology associated with decision tree
5 Visualizing a decision tree
6 References
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3. What is classification ?
What is classification ?
Classification is process for dividing a datasets into a different categories
or groups by adding label. Ex-
1 Checking a legitimate mail and spam mail by G-mail/Hot-mail etc.
2 Credit card notification (same card is used for very long distance) →
Fishy transaction.
3 Classification of fruits: Taste, size, color, etc
Types of classification
1 Decision tree
2 Random forest
3 Naive Baye’s
4 K-Nearest neighbor (KNN)
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4. Introduction to decision tree
Decision tree
⇒ Graphical representation of all the possible solution to a decisions.
⇒ Decisions are based on some conditions.
⇒ Decision made can be easily explained.
Note: Decision tree can also be used for the solving of regression problem.
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5. SBI card customer care IVR flow
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6. Eye disease detection
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7. Playing Tennis
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8. Banking system
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9. Terminology associated with decision tree
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10. Continued–
Branch node: The node, where other branches or nodes are connected.
Node 1, 2
Leaf node: The node, where other branches or nodes are not connected.
Node 3, 4, 5
Node 1 is called as root node.
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11. Classification and regression trees (CART) algorithm
How does a tree decide, where to split:
GINI index
Information gain
Reduction in variance
Chi-square
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12. References
E. Alpaydin, Introduction to machine learning. MIT press, 2020.
J. Grus, Data science from scratch: first principles with python. O’Reilly Media,
2019.
T. M. Mitchell, The discipline of machine learning. Carnegie Mellon University,
School of Computer Science, Machine Learning , 2006, vol. 9.
S. Haykin, Neural Networks and Learning Machines, 3/E. Pearson Education
India, 2010.
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