Book Image

F# for Machine Learning Essentials

By : Sudipta Mukherjee
Book Image

F# for Machine Learning Essentials

By: Sudipta Mukherjee

Overview of this book

The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs. If you want to learn how to use F# to build machine learning systems, then this is the book you want. Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data.
Table of Contents (16 chapters)
F# for Machine Learning Essentials
About the Author
About the Reviewers

Multiple linear regression and variations using Math.NET

As mentioned earlier, a regression model can be found using matrix decomposition such as QR and SVD.

The following code finds the theta from QR decomposition for the same data:

let qrlTheta = MultipleRegression.QR( created2 ,Y_MPG)

When run, this shows the following result in the F# interactive:

Now using these theta values, the predicted miles per gallon for the unknown car can be found by the following code snippet:

let mpgPredicted = qrlTheta * vector[8.;360.;215.;4615.;14.]

Similarly, SVD can be used to find the linear regression coefficients as done for QR:

let svdTheta = MultipleRegression.SVD( created2 ,Y_MPG)