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Home > Video lecture > CSE > Artificial Intelligence(Prof. S. Sarkar Prof. Anupam Basu) > Rule Induction and Decision Trees – II | Artificial Intelligence | Video lecture

Rule Induction and Decision Trees – II | Artificial Intelligence | Video lecture

 Rule Induction and Decision Trees РII

TOPICS:

Artificial Intelligence1:11
Instructional Objectives
ID3
Data is sometimes noisy
Practical issues with Decision Trees
Data is sometimes noisy
Evaluating Performance Accuracy
Overfitting
overfitting the Data
overfitting the Data: definition
Causes for Overfitting the Data
Stopping
Pruning via cross-validation
Pruning via Cross-Validation
Training and Validation
Decision Tree Pruning
Reduced Error Pruning
Example
Process continues until no improvement
Advantages of Rule Post-Pruning
Methods to Validate the New Tree
fl Methods to Validate the New Tree
Methods to Validate the New Tree
Discretizing Continuous Attributes
Missing Attribute Values
Continuous Valued Attributes

 

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