Overview Understanding key machine learning algorithms is crucial for solving real-world data problems effectively.Data scientists should master both supervised ...

Common regression techniques include multiple linear regression, tree-based regression (decision tree, AdaBoost, random forest, bagging), neural network regression, and k-nearest neighbors (k-NN) ...

Linear techniques include ordinary linear regression, L1 (lasso) and L2 (ridge) regression, and linear support vector regression (linear SVR). This article presents a demo of linear SVR, implemented ...

What is linear regression? Linear regression is a basic machine learning algorithm that is used for predicting a variable based on its linear relationship between other independent variables.

Equivariant high-breakdown point regression estimates are computationally expensive, and the corresponding algorithms become unfeasible for moderately large number of regressors. One important advance ...

Linear regression draws corresponding trend lines, such as disease outbreaks, bitcoin prices, demand for software experts, etc.

This paper develops some efficient algorithms for linear spline and piecewise multiple linear regression. A plotting procedure that shows the existence and location of changes in linear regression ...