NumPy Tutorial · NumPy Random
Logistic Distribution
Learn all about Logistic Distribution in this comprehensive tutorial.
5 min read advanced
- •Logistic Distribution is used to describe growth.
- •Both distributions are near identical, but logistic distribution has more area under the tails, meaning it represents more possibility of occurrence of an event further away from mean.
Logistic Distribution
Logistic Distribution is used to describe growth.
Used extensively in machine learning in logistic regression, neural networks etc.
It has three parameters:
loc - mean, where the peak is. Default 0.
scale - standard deviation, the flatness of distribution. Default 1.
size - The shape of the returned array.
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Visualization of Logistic Distribution
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Difference Between Logistic and Normal Distribution
Both distributions are near identical, but logistic distribution has more area under the tails, meaning it represents more possibility of occurrence of an event further away from mean.
For higher value of scale (standard deviation) the normal and logistic distributions are near identical apart from the peak.
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