Objective

The objective of any supervised classification learning algorithm is to predict the correct class with higher probability. Therefore, for each given , we have to calculate the predicted output, that is, the probability . Therefore, .

Referring to binary logistic regression in the preceding diagram:

• Predicted output, that is, . Here, the sigmoid function shrinks the value of between 0 and 1.

• This means, when , the sigmoid function of this, that is .
  

• When , the sigmoid function of this, that is, .

Once we have calculated , that is, the predicted output, we are done with our forward propagation task. Now, we will calculate the error value using the cost function and try to backpropagate to minimize our error value by changing the values of our parameters, W and b, through gradient descent.