May 1, 2024
4 minute read
Cross-validation is a technique used in machine learning to assess the performance of a model. It involves partitioning the data into multiple subsets, or folds, and iteratively training and evaluating the model on different combinations of these folds. This process helps to reduce the impact of chance fluctuations in the data and provides a more robust estimate of the model's performance.
Why Learn Cross-Validation?
There are several reasons why you might want to learn cross-validation:
vmek7y|
Find a path to becoming a Cross Validation. Learn more at:
OpenCourser.com/topic/vmek7y/cross
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Cross Validation.
Provides a comprehensive overview of cross-validation techniques, covering both theoretical foundations and practical applications. It is written by David M. Powers, a leading expert in the field, and includes numerous examples and case studies.
Provides a comprehensive overview of cross-validation techniques, with a focus on practical applications. It includes numerous examples and case studies.
This classic textbook covers a wide range of machine learning topics, including cross-validation. It is written by three leading researchers in the field and provides a deep understanding of the theoretical foundations of cross-validation.
Covers cross-validation in the context of sparse learning. It is written by three leading researchers in the field and provides a deep understanding of the theoretical foundations of cross-validation in this context.
Practical guide to machine learning using Python. It covers cross-validation in Chapter 4.
Covers cross-validation in the context of ensemble learning. It provides a comprehensive overview of ensemble learning techniques and their applications.
This textbook more accessible introduction to statistical learning than Hastie et al's The Elements of Statistical Learning. It covers cross-validation in Chapter 7.
Practical guide to machine learning using Python. It covers cross-validation in Chapter 6.
Practical guide to machine learning using R. It covers cross-validation in Chapter 7.
Practical guide to machine learning using Java. It covers cross-validation in Chapter 6.
Covers cross-validation in the context of deep learning. It is written by three leading researchers in the field and provides a deep understanding of the theoretical foundations of cross-validation in this context.
Practical guide to machine learning. It covers cross-validation in Chapter 6.
Practical guide to machine learning for non-experts. It covers cross-validation in Chapter 6.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/vmek7y/cross
Save
