Answer-'Logistic regression' is:

Logistic Regression uses the sigmoid function to map any real-valued input to a probability between 0 and 1 using PY1 11 e-z. A threshold typically 0.5 converts probabilities to class predictions. Output is interpretable as log-odds coefficients show feature importance and direction. It assumes linear relationship between features and log-odds. Logistic regression is used in banking for Binary credit decisions approvereject, fraud detection fraudnot fraud and churn prediction leavestay. It can be extended to multi-class via softmax multinomial logistic regression. Despite its name, it is a classification, not a regression, algorithm.