Using logistic regression to classify the relative likelihood of two outcomes

Logistic regression is a statistical modeling technique used to predict categorical outcomes, particularly in binary classification situations where the outcome can take one of two possible values. It aims to find the relationship between a set of input variables and the probability of a certain outcome occurring. Logistic regression estimates the relationship between these input variables and the probability of the outcome. It tries to find the best-fit line or curve that represents this relationship. Unlike linear regression, which predicts continuous values, logistic regression predicts the probability of a specific outcome. We set a probability threshold (usually 0.5) to decide the class label. If the predicted probability is above the threshold, the outcome is predicted as one class, and if it is below the threshold, the outcome is predicted as the other class. Once the model is trained, it can be used...