Question

What is the difference between a Perceptron and Logistic Regression in Digital learning?

Answer 1

A Multi-Layer Perceptron (MLP) is one of the most basic neural networks that we use for classification. For a binary classification problem, we know that the output can be either 0 or 1. This is just like our simple logistic regression, where we use a logit function to generate a probability between 0 and 1.

So, what’s the difference between the two?

Simply put, it is just the difference in the threshold function! When we restrict the logistic regression model to give us either exactly 1 or exactly 0, we get a Perceptron model:

Answer 2

A Multi-Layer Perceptron (MLP) is one of the most basic neural networks that we use for classification. For a binary classification problem, we know that the output can be either 0 or 1. This is just like our simple logistic regression, where we use a logit function to generate a probability between 0 and 1.

So, what’s the difference between the two?

Simply put, it is just the difference in the threshold function! When we restrict the logistic regression model to give us either exactly 1 or exactly 0, we get a Perceptron model:

Answer 3

A Multi-Layer Perceptron (MLP) is one of the most basic neural networks that we use for classification. For a binary classification problem, we know that the output can be either 0 or 1. This is just like our simple logistic regression, where we use a logit function to generate a probability between 0 and 1.So, what’s the difference between the two?

Simply put, it is just the difference in the threshold function! When we restrict the logistic regression model to give us either exactly 1 or exactly 0, we get a Perceptron model: