Statistical Analysis
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Adapted from an appendix of my MS thesis. Statistical Analysis Internal Validity Train and test splits, cross-validation, and the like seek to estimate the expected generalization performance of a learning procedure. Keeping test data rigorously independent from algorithm development minimizes the bias of this estimation. However, there are multiple sources of arbitrary variations in these estimates. A systematic study of machine learning benchmarks shows that their most important sources of…
1Key Takeaways
- Adapted from an appendix of my MS thesis.
- Statistical Analysis Internal Validity Train and test splits, cross-validation, and the like seek to estimate the expected generalization performance of a learning procedure.
- Keeping test data rigorously independent from algorithm development minimizes the bias of this estimation.
- However, there are multiple sources of arbitrary variations in these estimates.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that adapted from an appendix of my MS thesis.
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