Mar 18, 2025 · Mar 18, 2025 · Mar 18, 2025 · Mar 18, 2025
diff --git a/galleries/examples/scales/logit_demo.py b/galleries/examples/scales/logit_demo.py
 ===========

 Examples of plots with logit axes.

 This example visualises how ``set_yscale("logit")`` works on probability plots
 by generating three distributions: normal, laplacian, and cauchy in one plot.

 The advantage of logit scale is that it effectively spreads out values close to 0 and 1.

 In a linear scale plot, probability values near 0 and 1 appear compressed,
 making it difficult to see differences in those regions.

 In a logit scale plot, the transformation expands these regions,
 making the graph cleaner and easier to compare across different probability values.

 This makes the logit scale especially useful when visalising probabilities in logistic
 regression, classification models, and cumulative distribution functions.
 """

 import math
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		===========

		Examples of plots with logit axes.

		This example visualises how ``set_yscale("logit")`` works on probability plots
		by generating three distributions: normal, laplacian, and cauchy in one plot.

		The advantage of logit scale is that it effectively spreads out values close to 0 and 1.

		In a linear scale plot, probability values near 0 and 1 appear compressed,
		making it difficult to see differences in those regions.

		In a logit scale plot, the transformation expands these regions,
		making the graph cleaner and easier to compare across different probability values.

		This makes the logit scale especially useful when visalising probabilities in logistic
		regression, classification models, and cumulative distribution functions.
		"""

		import math
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