Is Stochastic gradient descent a classifier or an optimizer?

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I am new to Machine Learning and I am trying analyze the classification algorithm for a project of mine. I came across SGDClassifier in sklearn library. But a lot of papers have referred to SGD as an optimization technique. Can someone please explain how is SGDClassifier implemented?

Taken from SGD sikit-learn documentation

loss="hinge": (soft-margin) linear Support Vector Machine, loss="modified_huber": smoothed hinge loss, loss="log": logistic regression

1.5. Stochastic Gradient Descent — scikit-learn 0.23.1 documentation, Stochastic gradient descent is a very popular and common algorithm used in various Machine Learning algorithms, most importantly forms the� Stochastic gradient descent is a stochastic approximation of the gradient descent optimization method for minimizing an objective function that is written as a sum of differentiable functions. In other words, SGD tries to find minima or maxima by iteration. – seralouk Aug 2 '17 at 9:50

SGD is indeed a technique that is used to find the minima of a function. SGDClassifier is a linear classifier (by default in sklearn it is a linear SVM) that uses SGD for training (that is, looking for the minima of the loss using SGD). According to the documentation:

SGDClassifier is a Linear classifiers (SVM, logistic regression, a.o.) with SGD training.

This estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). SGD allows minibatch (online/out-of-core) learning, see the partial_fit method. For best results using the default learning rate schedule, the data should have zero mean and unit variance.

This implementation works with data represented as dense or sparse arrays of floating point values for the features. The model it fits can be controlled with the loss parameter; by default, it fits a linear support vector machine (SVM).

Stochastic gradient descent, Welcome to SE:Data Science. SGD is a optimization method, while Logistic Regression (LR) is a machine learning algorithm/model. You can� 1.5. Stochastic Gradient Descent¶ Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to fitting linear classifiers and regressors under convex loss functions such as (linear) Support Vector Machines and Logistic Regression. Even though SGD has been around in the machine learning community for a long time, it has received

SGDClassifier is a linear classifier which implements regularized linear models with stochastic gradient descent (SGD) learning

Other classifiers:

classifiers = [
("ASGD", SGDClassifier(average=True, max_iter=100)),
("Perceptron", Perceptron(tol=1e-3)),
("Passive-Aggressive I", PassiveAggressiveClassifier(loss='hinge',
                                                     C=1.0, tol=1e-4)),
("Passive-Aggressive II", PassiveAggressiveClassifier(loss='squared_hinge',
                                                      C=1.0, tol=1e-4)),
("SAG", LogisticRegression(solver='sag', tol=1e-1, C=1.e4 / X.shape[0]))

]

Stochastic Gradient Descent (sgd) is a solver. It is a simple and efficient approach for discriminative learning of linear classifiers under convex loss functions such as (linear) Support Vector Machines and Logistic Regression.

Other alternative solvers for sgd in neural_network.MLPClassifier are lbfgs and adam

solver : {‘lbfgs’, ‘sgd’, ‘adam’}, default ‘adam’

The solver for weight optimization.

‘lbfgs’ is an optimizer in the family of quasi-Newton methods

‘sgd’ refers to stochastic gradient descent.

‘adam’ refers to a stochastic gradient-based optimizer proposed by Kingma, Diederik, and Jimmy Ba

Details about implementation of SGDClassifier can be read @ SGDClassifier documentation page.

in brief:

This estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). SGD allows minibatch (online/out-of-core) learning

Stochastic Gradient Descent — Clearly Explained !!, Gradient Descent is a popular optimization technique in Machine at random for each iteration, the path taken by the algorithm to reach the� Before explaining Stochastic Gradient Descent (SGD), let’s first describe what Gradient Descent is. Gradient Descent is a popular optimization technique in Machine Learning and Deep Learning, and it can be used with most, if not all, of the learning algorithms.

What is the difference between SGD classifier and the Logisitc , Stochastic Gradient Descent (SGD) is a simple yet efficient optimization algorithm used to find the values of parameters/coefficients of functions that minimize a� Gradient descent is the preferred way to optimize neural networks and many other machine learning algorithms but is often used as a black box. This post explores how many of the most popular gradient-based optimization algorithms such as Momentum, Adagrad, and Adam actually work.

ML, Using the Gradient Decent optimization algorithm, the weights are updated incrementally after each epoch (= pass over the training dataset). Compatible cost� The only difference between vanilla gradient descent and Stochastic Gradient Descent is the addition of the next_training_batch function. Instead of computing our gradient over the entire data set, we instead sample our data, yielding a batch . We then evaluate the gradient on this batch and update our weight matrix W.

Scikit Learn - Stochastic Gradient Descent, To find the weights that minimize our cost function, we can use optimization algorithm called gradient descent: GradientDescentCostFunction.png. Stochastic gradient descent is a very popular and common algorithm used in various Machine Learning algorithms, most importantly forms the basis of Neural Networks. In this article, I have tried my best to explain it in detail, yet in simple terms.

Comments
  • Stochastic gradient descent is a stochastic approximation of the gradient descent optimization method for minimizing an objective function that is written as a sum of differentiable functions. In other words, SGD tries to find minima or maxima by iteration.