Last updated on Oct. 12, 2021, 7:23 a.m.

**Step 1:** Initialize the basic class functions including initialization of regularization parameter `C`

and number of iterations.

**Step 2:** Implement the Objective function i.e; Hinge Loss Function.

**Step 3:** After implementing the Hinge Loss Objective function, we will implement the Subgradient calculation function. Now as we know the Hinge loss function is not differentiable, but it is convex.

**Step 4:** Now we can implement the fit function, which will take in input `X`

and `y`

and learn the `w`

and `b`

parameters using objective function and Subgradient descent algorithm.

**Step 5:** Lastly, we can implement the predict function.

Write a sample python code for mini-batch generation. Top 17 frequently asked Machine Learning Interview Questions with Answers.