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

Plotting the result of multiple linear regression

Using FsPlot, I plotted the data of the cars and the miles per gallon as a scatter plot:

So for each data point, I plotted a dot and then plotted the predicted value of the miles per gallon (the prediction was performed using multiple linear regression).

The following code renders the chart:

As a measure to find out whether the model is working better or not, you can find out the average residual value. A residual is the difference between the actual value and the predicted value. So for our example of miles per gallon dataset for multiple linear regression, the average residual can be calculated as follows:

When executed in the F# interactive, this produces the following output. To save space, I have taken only the first five rows.

From this you can see that for the first record, the actual MPG value was 18 and the predicted value was 15.377 roughly. So the residual for this entry is about 2.623. The smaller the average residual, the better the...