《Machine Learning（Tom M. Mitchell）》读书笔记——6、第五章_csdn tom mitchell machine learning-优快云博客

本文探讨了机器学习中假设评估的重要性，介绍了样本误差与真实误差的概念，并详细讨论了置信区间估计、采样理论基础等内容。此外，还涉及两种假设之间的误差差异及如何比较不同的学习算法。

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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