Applications of Permutation Tests in Machine Learning
Permutation tests find diverse applications in machine learning, including:
- Assessing the significance of feature importance scores in predictive models.
- Evaluating the performance difference between machine learning algorithms or configurations.
- Testing for the presence of correlations between variables in datasets.
- Validating the effectiveness of preprocessing techniques or data transformations.
Permutation tests in Machine Learning
Permutation tests become quite useful in these situations, offering a reliable and adaptable substitute for hypothesis testing. The basic idea behind permutation testing is to generate a null distribution by randomly permuting the observed data. This allows for inference to be made without making strict assumptions about the distribution of the data. This article aims to demystify permutation tests in machine learning.
Table of Content
- What are Permutation Tests?
- Permutation Tests vs Traditional Parametric Tests
- Estimating the p-value in Permutation Tests
- P-values and its Interpretation in Permutation Tests
- Permutation Test in Python
- Benefits and Limitations of Permutation Test
- Applications of Permutation Tests in Machine Learning