This is a detailed tutorial of NumPy Logistic Distribution. Learn to implement Logistic Distribution in NumPy and visualize using Seabor

This is a detailed tutorial of NumPy Logistic Distribution. Learn to implement Logistic Distribution in NumPy and visualize using Seabor

These distributions help us in describing the statistical growth of the data. It is known for predicting how the growth will happen by taking in certain data. These distributions are continuous in nature. These distributions do resemble the normal distribution in certain ways, but they have heavier tails. The application of such distribution is in the fields of physics, logistic regression, hydrology and also machine learning and neural networks.

The parameter that this distribution takes in as input are:

`loc`

– mean of the data which is 0 by default.`scale`

– it is the standard deviation which predicts the flatness of the distribution, which by default is 1.`size`

– it will depict the shape of the array.

So let us go through an example to understand it better:

we have given only the size. So as a result, the other parameters will be taken by default.

The difference is that the logistic distribution is heavier and they have more area under them.

I hope you found this guide useful. If so, do share it with others who are willing to learn Numpy and Python. If you have any questions related to this article, feel free to ask us in the comments section.

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