Explain Gradient Descent optimization in Logistic Regression

Gradient Descent is an optimization algorithm used to minimize the Logistic Regression loss function by iteratively updating model parameters.

Logistic Regression learns model parameters by minimizing a cost function called Log Loss (Cross Entropy Loss). Since there is no direct analytical solution for minimizing this loss in Logistic Regression, optimization algorithms such as Gradient Descent are used.

The central idea behind Gradient Descent is:

The Logistic Regression hypothesis is:

h(x) = 1 / (1 + e^-(w·x + b))

The loss function is:…