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

Baseline predictors

Before delving into true collaborative filtering, let's look at some baseline predictors that can predict ratings for new users who haven't rated anything yet, which makes it almost impossible to find out the neighborhood of such users. For such users, a basic baseline rating can be the average of all ratings. The problem with applying collaborative filtering in order to predict the ratings of items for new users is referred to as Cold Start in collaborative filtering literature.

The baseline predictor is normally denoted by for user and item . The base case where the baseline is set as equal to the global average of all ratings is given by the following formula:

However, this can be optimized using the average of that user's rating for other items (if any are available) or the average rating for that particular item (given by all other users). In these two cases, the baseline predictor is calculated using the following formulae:

Note that the subscript is used to denote...