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F# for Machine Learning Essentials

You're reading from  F# for Machine Learning Essentials

Product type Book
Published in Feb 2016
Publisher
ISBN-13 9781783989348
Pages 194 pages
Edition 1st Edition
Languages
Author (1):
Sudipta Mukherjee Sudipta Mukherjee
Profile icon Sudipta Mukherjee

Table of Contents (16) Chapters

F# for Machine Learning Essentials
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Introduction to Machine Learning Linear Regression Classification Techniques Information Retrieval Collaborative Filtering Sentiment Analysis Anomaly Detection Index

Linear regression method of least square


Let's say you have a list of data point pairs such as the following:

You want to find out if there are any linear relationships between and .

In the simplest possible model of linear regression, there exists a simple linear relationship between the independent variable (also known as the predictor variable) and the dependent variable (also known as the predicted or the target variable). The independent variable is most often represented by the symbol and the target variable is represented by the symbol . In the simplest form of linear regression, with only one predictor variable, the predicted value of Y is calculated by the following formula:

is the predicted variable for . Error for a single data point is represented by:

and are the regression parameters that can be calculated with the following formula.

The best linear model minimizes the sum of squared errors. This is known as Sum of Squared Error (SSE).

For the best model, the regression...

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