《Machine Learning（Tom M. Mitchell）》读书笔记——6、第五章

1. Introduction (about machine learning)

2. Concept Learning and the General-to-Specific Ordering

3. Decision Tree Learning

4. Artificial Neural Networks

5. Evaluating Hypotheses

6. Bayesian Learning

7. Computational Learning Theory

8. Instance-Based Learning

9. Genetic Algorithms

10. Learning Sets of Rules

11. Analytical Learning

12. Combining Inductive and Analytical Learning

13. Reinforcement Learning

5. Evaluating Hypotheses

作为理科生，概率论是基础，就不细说了！

5.1 MOTIVATION 

In many cases it is important to evaluate the performance of learned hypotheses as precisely as possible. 

5.2 ESTIMATING HYPOTHESIS ACCURAC

5.2.1 Sample Error and True Error

5.2.2 Confidence Intervals for Discrete-Valued Hypotheses

5.3 BASICS OF SAMPLING THEORY

5.3.1 Error Estimation and Estimating Binomial Proportions

5.3.2 The Binomial Distribution

5.3.3 Mean and Variance

5.3.4 Estimators, Bias, and Variance

5.3.5 Confidence Intervals

5.3.6 Two-sided and One-sided Bound

5.4 A GENERAL APPROACH FOR DERIVING CONFIDENCE INTERVALS

5.4.1 Central Limit Theorem

5.5 DIFFERENCE IN ERROR OF TWO HYPOTHESES 

5.5.1 Hypothesis Testing 

5.6 COMPARING LEARNING ALGORITHMS

5.6.1 Paired t Tests 

5.6.2 Practical Consideration