Stochastic Gradient Descent Regressor FAQs

Q. What is Stochastic Gradient Descent (SGD)?

Stochastic Gradient Descent is an optimization algorithm commonly used in machine learning for training models. It’s an iterative method for minimizing an objective function, often used in scenarios where the dataset is large.

Q . How does SGD differ from standard Gradient Descent?

Standard Gradient Descent computes the gradient of the cost function using the entire dataset, while SGD computes the gradient using a single or a small subset of the dataset (mini-batch) at each iteration. This makes SGD computationally efficient and suitable for large datasets.

Q .When should I use SGD for regression tasks?

SGD is particularly useful when dealing with large datasets, as it’s computationally efficient and can handle large amounts of data that don’t fit into memory. It’s also suitable for online learning scenarios where data arrives sequentially.

Stochastic Gradient Descent (SGD) is a popular optimization technique in the field of machine learning. It is particularly well-suited for handling large datasets and online learning scenarios where data arrives sequentially. In this article, we will discuss how a stochastic gradient descent regressor is implemented using Scikit-Learn.