Quiz and summary
Contents
5.3. Quiz and summary¶
5.3.1. Quiz 5¶
Complete Quiz 5 to check your understanding of this topic. You are advised to score at least 50% to proceed to the next topic.
5.3.2. Summary¶
How to evaluate your machine learning model is a key question. In this chapter, we learnt about the two most common methods of evaluating a model, cross-validation and bootstrap. We started with cross-validation, learning how to split the data into training and validation sets, and how to use the training set to fit a model and the validation set to evaluate the model. The more intensively you cross-validate your data, the less biased your estimation of the model’s true performance. Then, we moved on to bootstrap, learning how to estimate the uncertainty of our model.
In this topic, you learnt:
The importance of cross-validation in evaluating your model.
How to use Random Validation Set, Leave-One-Out Cross Validation and K-Fold Cross Validation
How to use boostrapping the estimate the uncertainty of your model’s fit.
5.3.3. References and further reading¶
This material is based on the following resources:
The Textbook: James G, Witten D, Hastie T, Tibshirani R., An Introduction to Statistical Learning, Second Edition, Springer, 2021. PDF [James et al., 2021]