Sigmoid Function Explained in Less than 5 Minutes
One of the most popular functions is the Sigmoid, a powerful function mainly we’re talking about classification problems.
Basically, the Sigmoid Function returns a value between 1 and 0, this is too powerful for Binary Classification Problems.
But how we can interpret a value returned by a Sigmoid Function?
Intepreting Values Return by a Sigmoid Function
Suppose you trained a Neural Network to classify images of Cats and Dogs, classic problem, where Dog is 1 and Cat is 0. Basically, when your Model returns a values ≥ 0.51 it means the image is of a Dog, and < 0.51 means the image is of a Cat.
The Sigmoid Function Formula is:
Where:
- e: Is the Number of Euler
- x: Can be the output of your Neural Network
As you can see, the number 1 as the numerator makes impossible the function returns a value > 1.
When you predict a Dataset using a Sigmoid Function, your result will looks like:
I hope you have managed to understand how Overfitting works!
For now, this is all!
See you next time!
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