Ordinary Least Squares Linear Regression Algorithm
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An Ordinary Least Squares Linear Regression Algorithm is a least squares linear regression algorithm that is a ordinary least squares algorithm and that can be implemented by an ordinary least-squares linear regression system to solve an ordinary least-squares linear regression task (that minimizes the sum of squared distances between the observed response variables and the regressed model's fitted response variables against some regression dataset) whose input is an un-regularized linear regression model.
- AKA: Un-Regularized Linear Least Squares Regression Method.
- Example(s):
- Counter-Example(s):
- See: Convex Optimization Algorithm.
References
2014
- https://sebastianraschka.com/faq/docs/closed-form-vs-gd.html
- QUOTE: Fitting a model via closed-form equations vs. Gradient Descent vs Stochastic Gradient Descent vs Mini-Batch Learning. What is the difference?