Linear regression In some cases, linear regression doesn’t even require an optimizer, since it is solvable in closed form. Otherwise, it is easily optimized using gradient descent (see below).

Nonlinear regression algorithms, which fit curves that are not linear in their parameters to data, are a little more complicated, because, unlike linear regression problems, they can’t be solved ...

And while a multi-linear regression model is not perfect given the need to rely on historic data and the amount of maintenance over time that can otherwise create a bias in the results, it is ...

Researchers at EPFL have created a mathematical model that helps explain how breaking language into sequences makes modern AI-like chatbots so good at understanding and using words. The work is ...

This type of statistical analysis consists of examining various data points to determine which variables are most notable predictors. Linear regression draws corresponding trend lines, such as ...

A new study investigated how logistic regression model training affects performance, and which features are best to include when examining datasets from individuals suffering from COVID-19.

Using Linear Regression Algorithm in Intel Data Analytics Acceleration Library. This section shows how to invoke the linear regression method in Python6 using Intel DAAL.