The demo creates a linear support vector regression model, evaluates the model accuracy on the training and test data, and then uses the model to predict the target y value for x = [-0.1660, 0.4406, ...

This project consists of a deep dive on multiple linear regression (OLS) and its regularized variants (Ridge, the Lasso, and the Elastic Net) as well as Python implementations for exploratory data ...

Identification of uncertain and nonlinear systems is an important and challenging problem. Fuzzy models of the Takagi-Sugeno (TS) type may be a good choice to describe such systems; however, in many ...

Researchers at EPFL have created a mathematical model that helps explain how breaking language into sequences makes modern AI ...

The benefits of regression analysis are manifold: The regression method of forecasting is used for, as the name implies, forecasting and finding the causal relationship between variables.

Learn how to detect outliers, how they impact your regression coefficients, and how to trim or winsorize them. Compare the pros and cons of each method.

In these cases, OLS should not be used and the literature suggests carrying out orthogonal distance regression (ODR). 5,6 The aim of this work is to suggest how to handle such cases in which errors in ...

In a fuzzy regression discontinuity (RD ... when the 2SLS estimate is close to (far from) the OLS estimate. Here, we show that a similar problem arises in Fuzzy RD. Hence, if the endogeneity bias is ...