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Why Sigmoid in Logistic Regression?
The origin of sigmoid from a probabilistic perspective.
Logistic regression returns the probability of a binary outcome (0 or 1).
We all know logistic regression does this using the sigmoid function.
In other words, have you ever wondered why we use Sigmoid in logistic regression?
The most common reason we get to hear is that Sigmoid maps all real values to the range [0,1].
But there are infinitely many functions that can do that.
What is so special about Sigmoid?
What’s more, how can we be sure that the output of Sigmoid is indeed a probability?
See, as discussed above, logistic regression output is interpreted as a probability.
But this raises an essential question: “Can we confidently treat the output of sigmoid as a genuine probability?”
It is important to consider that not every numerical value lying within the interval of [0,1] guarantees that it is a legitimate probability.
In other words, just outputting a number between [0,1] isn’t sufficient for us to start interpreting it as a probability.
Instead, the interpretation must stem from the formulation of logistic regression and its assumptions.
So where did the Sigmoid come from?
If you have never understood this, then…
This is precisely what we are discussing in this today’s article, which is available for free for everyone.
We are covering:
The common misinterpretations that explain the origin of Sigmoid.
Why are these interpretations wrong?
What an ideal output of logistic regression should look like.
How to formulate the origin of Sigmoid using a generative approach under certain assumptions.
What if the assumptions don’t hold true.
How the generative approach can be translated into the discriminative approach?
Best practices while using generative and discriminative approaches.
Hope you will get to learn something new :)
The article is available for free to everyone.
👉 Interested folks can read it here: Why Do We Use Sigmoid in Logistic Regression?
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