Cross-validation
5 important questions on Cross-validation
What are folds? What is their usage?
What is problematic about a biased validation set in the process of training a model?
How may bias occur in the validation process? Name 2 possibilities.
- The validation data shows a lot of similarity with the training data.
- The occurrence of different classes is not representative to the real world: There is bias in the class distribution.
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How is the best parameter setting determined in cross-validation?
How to ensure balance between the classes during training a supervised classifier?
1. Reflect the distribution of classes to the occurrence in real-life.
2. Apply stratification: Have an equal amount of items per class and afterwards
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