Logistic regression is a simple yet powerful model that solves the linear classification or binary classification problem. Due to its simplicity, the algorithm is widely used in the practical industrial field. Although the model is easy to implement, it has enormous power, which can be demonstrated through a linearly separable dataset.
A logistic regression model is generally described as the linear relationship between the input vector and its parameters. Let's take a look at how the model is formulated:
are conditional probabilities that represent how the input vector belongs to the target class. For instance, if 
is 0.9, then x is highly likely to belong to the 
class. In this case, there are only two target classes, and so the sum of them must always be 1. 
is a logistic sigmoid function that returns a value between 0 and 1. This function...
