Unsupervised machine learning ingests unlabeled data—lots and lots of it—and uses algorithms ... regression, and ranking are examples of supervised learning, which constitutes a majority of machine ...

Linear regression is a statistical method that models the relationship between a dependent variable (also called the target or the outcome) and one or more independent variables (also called the ...

Matrix Operations (Transpose/Multiplication/Inversion) Upper & Lower triangular matrices LU - Decomposition (ref: http://nptel.ac.in/courses/108108079/pdf/Unit%201 ...

Non-linear regression modeling is common ... We have implemented the algorithm as part of an open-source R-package, knutar. The standard process for placing knots for RCS regression is by a regular ...

Abstract: Comparison of the innovative Linear Regression and Logistic Regression Algorithms for Ground Water Level Detection with Improved Accuracy is the goal of this study, which was designed to ...

Abstract: This article proposes an algorithm for solving multivariate regression and classification problems using piecewise linear predictors over a polyhedral partition of the feature space. The ...

Linear least-squares regression is fundamental in the analysis of data, such as that generated from biology, econometrics, engineering, physics, and many other technical disciplines. The present ...

For example, sorting algorithms turn unordered data into data ordered by some criteria, often the numeric or alphabetical order of one or more fields in the data. Linear regression algorithms fit ...