SGD

Stochastic gradient descent (SGD) is a handy ML tool for finding the best answer to a problem. It’s especially useful when dealing with big piles of data or complicated models. Think of it like this: imagine you’re fixing a recipe and want the perfect taste. Instead of trying the whole recipe at once, you taste a bit, make a tiny adjustment, and repeat. That’s how SGD works. It makes little changes to the model’s settings step by step to find the best result, using just a small part of the data each time.

SGD can be used for a variety of supervised learning tasks such as regression, classification, and even deep learning. It’s commonly used to train linear models, such as linear regression and logistic regression, but it can also be employed with nonlinear models by using the appropriate kernel functions.

One thing that’s important to keep in mind about SGD is that it’s sensitive to the scale of the data, so we need to scale our data before using it.

In this chapter, we’ll explore the use of SGD for both regression and classification tasks. We’ll learn about the two main types of SGD algorithms available in scikit-learn: stochastic gradient descent classifier (SGDClassifier) and stochastic gradient descent regressor (SGDRegressor). Additionally, we’ll delve into the key hyperparameters within SGD, exploring its strengths and limitations.

The SGDClassifier model

The SGDClassifier is a linear classification ...