《机器学习基础》是由台湾大学教授林轩田主讲的一门课程，系统地介绍了机器学习的基本理论和方法。该课程适合初学者入门，并为深入研究打下坚实的基础。
Lecture 15: Validation
Model Selection Problem
The model selection problem can be dangerous if handled improperly, and dishonest practices in testing (Etest) can lead to misleading results.
Validation involves selecting a model based on its performance on the training data (Eval(Dtrain)) while ensuring it generalizes well to unseen data by returning Am*(D).
Leave-One-Out Cross Validation provides an almost unbiased estimate but requires significant computational resources.
V-Fold Cross Validation offers a balance between computation and performance, making it more practical for many applications.
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