8000 Merge pull request #29769 from meeseeksmachine/auto-backport-of-pr-29… · matplotlib/matplotlib@9800491 · GitHub
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Commit 9800491

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Merge pull request #29769 from meeseeksmachine/auto-backport-of-pr-29767-on-v3.10.1-doc
Backport PR #29767 on branch v3.10.1-doc (Add description to logit_demo.py script)
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galleries/examples/scales/logit_demo.py

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===========
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Examples of plots with logit axes.
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This example visualises how ``set_yscale("logit")`` works on probability plots
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by generating three distributions: normal, laplacian, and cauchy in one plot.
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The advantage of logit scale is that it effectively spreads out values close to 0 and 1.
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In a linear scale plot, probability values near 0 and 1 appear compressed,
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making it difficult to see differences in those regions.
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In a logit scale plot, the transformation expands these regions,
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making the graph cleaner and easier to compare across different probability values.
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This makes the logit scale especially useful when visalising probabilities in logistic
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regression, classification models, and cumulative distribution functions.
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"""
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import math

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