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

Ranking accuracy metrics

A third view of the task of a recommender system is that it ranks all items with respect to a user (or ranks all user-item pairs), such that the higher-ranked recommendations are more likely to be relevant to users. Individual rating predictions may be incorrect, but, as long as the order is caught correctly, rank accuracy measures will evaluate the system as having a high accuracy.

Prediction-rating correlation

If the variance of one variable can be explained by the variance in another, the two variables are said to correlate. Let be items and be their true order rank. Let the recommender system predict the ranks for these items (i.e., is the true rank of the item and is the predicted rank). Let be the mean of , and be the mean of . The Spearman's correlation is defined as follows:

The following code finds the coefficient:

Get the raw code at

This produces the following output:

val p : float = 0.9338995047...