Logistic regression is a classification algorithm used to assign observations to a discrete set of classes. Unlike linear regression which outputs continuous number values, logistic regression transforms its output using the logistic sigmoid function to return a probability value which can then be mapped to two or more discrete classes.
In this notebook, we explore the binary logistic regression. We change the clsssification problem with 10 classes into a binary classification problem by considering only the points from the classes 'ship' and 'car'.
we will implement the algorith from scatc using pytorch.