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Data mining
Unit 1
Association Rule Mining
Introduction to Association Rule Mining
Measures of Association Rule Interestingness
Advanced Association Rule Mining Techniques
Unit 2
Classification
Introduction to Classification
Classification Algorithms
Evaluation and Validation in Classification
Unit 3
Clustering
Introduction to Clustering
Types of Clustering Algorithms
Evaluation of Clustering
Unit 4
Reinforcement Learning
Introduction to Reinforcement Learning
Q-Learning
Deep Reinforcement Learning
Unit 2 • Chapter 3
Evaluation and Validation in Classification
Summary
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What is cross-validation used for in machine learning?
To compare different methods and see how well they work in practice
Finding the best machine learning method for heart disease prediction
Estimating parameters for logistic regression
Categorizing data using K nearest neighbors
Why is using all data for training and testing a bad idea?
Improves the performance of machine learning algorithms
Ensures accurate estimation of parameters
Allows for better comparison of methods
No data left for testing the method
What would be a slightly better approach than using all data for training?
Using 75% of data for training and 25% for testing
Training each method on a different subset of data
Dividing data into blocks based on attributes like chest pain
Using logistic regression for all training and testing
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Classification Algorithms