Problem-Dependent Resampling Techniques
By External Provider
About this course
This course is designed for data scientists, machine learning practitioners, and researchers who want to understand how resampling techniques must be adapted to the structure of the problem at hand. You will learn how standard validation methods such as cross-validation can fail when applied blindly, and how to design problem-dependent resampling strategies for spatial data, pair-input data, and other dependent observation structures. The course also covers spatial cross-validation, dependency-aware evaluation design, and statistical testing methods to assess whether performance estimates are reliable. By the end of the course, you will be able to choose and construct appropriate resampling strategies that reflect the true structure of your data and provide trustworthy performance estimates.
What you will learn
- Apply spatial cross-validation to account for spatial autocorrelation
- Adapt performance evaluation methods for structured data relationships
- Use statistical tests such as Wilcoxon and permutation tests to assess significance
- Critically evaluate reported machine learning performance results