This final equation is the logistic curve for Logistic regression. It models the non-linear relationship between x and y with an ‘S’-like curve for the probabilities that y =1 - that event the y occurs. In this example a and b represent the gradients for the logistic function just like in linear regression. The logit equation can then be expanded to handle multiple gradients. This gives more freedom with how the logistic curve matches the data. The multiplication of two vectors can then be used to model more gradient values and give the following equation: