![]() This article will not explain the machine learning algorithms in detail, but only demonstrate their usage in JavaScript. I highly recommend to take the Machine Learning course by Andrew Ng. In the following article, I want to guide you through the theory and implementation of logistic regression with gradient descent in JavaScript. For instance, a set of articles could be classified into different topics such as web development, machine learning or software engineering.Īn algorithm that could be used to solve a classification problem is called logistic regression. However, a classification problem can be extended to a multiclass classification problem going beyond the binary classification. The output would be a binary dependent variable, because it can be either 0 or 1. For instance, a classification problem could be to separate spam emails from useful emails or to classify transactions into fraudulent and not fraudulent. In contrast, in a classification problem an algorithm is trained to predict categorical values. This article doesn't recap those topics but applies them for logistic regression to solve a classification problem in JavaScript. Checkout the recent articles to understand the foundational knowledge about linear regression including the essential cost function and hypothesis to perform the gradient descent algorithm. ![]() Afterward, the algorithm can predict housing prices for houses not included in the training set. ![]() ![]() The algorithm is trained by using a training set. It can be housing prices in a specific area based on a feature set such as square meters or numbers of bedrooms. In a regression problem, an algorithm is trained to predict continuous values. A couple of my recent articles gave an introduction to machine learning in JavaScript by solving regression problems with linear regression using gradient descent or normal equation. ![]()
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