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F# for Machine Learning Essentials

You're reading from   F# for Machine Learning Essentials Get up and running with machine learning with F# in a fun and functional way

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Product type Paperback
Published in Feb 2016
Publisher
ISBN-13 9781783989348
Length 194 pages
Edition 1st Edition
Languages
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Author (1):
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Sudipta Mukherjee Sudipta Mukherjee
Author Profile Icon Sudipta Mukherjee
Sudipta Mukherjee
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Table of Contents (9) Chapters Close

Preface 1. Introduction to Machine Learning FREE CHAPTER 2. Linear Regression 3. Classification Techniques 4. Information Retrieval 5. Collaborative Filtering 6. Sentiment Analysis 7. Anomaly Detection Index

Putting it together with Math.NET and FsPlot


In this example, you will see how Math.NET and FsPlot can be used together to generate the linear regression coefficients and plot the result. For this example, we will use a known relation between Relative Humidity (RH) and Dew point temperature. The relationship between relative humidity and dew point temperature is given by the following two formulas:

Here, t and td are the temperatures in degrees Celsius.

td is dew point, which is a measure of atmospheric moisture. It is the temperature to which the air must be cooled in order to reach saturation (assuming the air pressure and the moisture content are constant).

Let's say the dew point is 10 degrees Celsius, then we can see how linear regression can be used to find a relationship between the temperature and RH.

The following code snippet generates a list of 50 random temperatures and then uses the formula to find the RH. It then feeds this data into a linear regression system to find the best...

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