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--- |
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title: Wilcoxon |
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emoji: 🤗 |
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colorFrom: blue |
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colorTo: green |
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sdk: gradio |
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sdk_version: 3.0.2 |
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app_file: app.py |
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pinned: false |
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tags: |
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- evaluate |
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- comparison |
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description: >- |
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Wilcoxon's test is a signed-rank test for comparing paired samples. |
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--- |
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# Comparison Card for Wilcoxon |
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## Comparison description |
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Wilcoxon's test is a non-parametric signed-rank test that tests whether the distribution of the differences is symmetric about zero. It can be used to compare the predictions of two models. |
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## How to use |
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The Wilcoxon comparison is used to analyze paired ordinal data. |
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## Inputs |
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Its arguments are: |
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`predictions1`: a list of predictions from the first model. |
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`predictions2`: a list of predictions from the second model. |
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## Output values |
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The Wilcoxon comparison outputs two things: |
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`stat`: The Wilcoxon statistic. |
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`p`: The p value. |
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## Examples |
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Example comparison: |
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```python |
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wilcoxon = evaluate.load("wilcoxon") |
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results = wilcoxon.compute(predictions1=[-7, 123.45, 43, 4.91, 5], predictions2=[1337.12, -9.74, 1, 2, 3.21]) |
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print(results) |
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{'stat': 5.0, 'p': 0.625} |
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``` |
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## Limitations and bias |
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The Wilcoxon test is a non-parametric test, so it has relatively few assumptions (basically only that the observations are independent). It should be used to analyze paired ordinal data only. |
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## Citations |
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```bibtex |
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@incollection{wilcoxon1992individual, |
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title={Individual comparisons by ranking methods}, |
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author={Wilcoxon, Frank}, |
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booktitle={Breakthroughs in statistics}, |
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pages={196--202}, |
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year={1992}, |
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publisher={Springer} |
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} |
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``` |
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