What function is commonly used in logistic regression to predict loan defaulters?

In logistic regression, the function commonly used to predict outcomes, such as loan defaulters, is the sigmoidal function. This function is particularly useful because it maps any input value into a range between 0 and 1, making it ideal for binary classification tasks where the goal is to predict the probability of a specific outcome (e.g., the probability that a borrower will default on a loan).

The sigmoid function takes the form of an "S"-shaped curve, which allows it to effectively handle probabilities. When applied in logistic regression, the function transforms a linear combination of input features (such as income, age, credit score) into a predicted probability that an instance belongs to a certain class (default or no default). The output can then be interpreted as the likelihood of the event (defaulting on a loan) occurring. This is why the sigmoidal function is integral to logistic regression models and particularly effective for such predictive tasks.

Other functions, such as linear, exponential, or quadratic, do not possess the same characteristics or interpretability in the context of probability prediction for binary outcomes. The linear function would predict unbounded values, the exponential function can grow rapidly leading to prediction issues, and the quadratic function can lead to outputs