Since the target column is categorical, it uses linear regression to create an odd function that is wrapped with a log function to use regression as a classifier. Hence, it is a type of classification technique and not a regression. It is derived from cost function.
Logistic regression is categorized as a classification technique rather than a regression because its primary goal is to predict the probability of an observation belonging to a certain class or category. It’s commonly used for binary classification problems where the output variable takes on only two possible outcomes (e.g., 0 or 1, yes or no).
The name “regression” in logistic regression can be misleading. It comes from the fact that logistic regression is based on the logistic function (also known as the sigmoid function), which is a regression model. However, logistic regression doesn’t actually predict a continuous outcome as traditional regression does. Instead, it predicts the probability that a given input belongs to a particular class.
The logistic function, or sigmoid function, is the key component of logistic regression. It’s defined as:
�(�)=11+�−�σ(x)=1+e−x1
This function takes any input �x and transforms it into a value between 0 and 1, which can be interpreted as a probability. In logistic regression, this function is applied to the linear combination of input features and their associated coefficients, and the result is interpreted as the probability of belonging to a certain class.