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

Evaluating recommendations

Understanding how good a collaborative filtering system is can be broadly determined by measuring three types of accuracy parameters, namely:

  • Prediction Accuracy Metrics

    These measures help to understand how accurately the recommender works. These measures work by calculating the differences between previously rated items and their ratings estimated by the recommender system.

  • Decision Support Metrics (a.k.a Confusion Matrix)

    These measures are used to find how well a supervised learning algorithm has performed.

  • Ranking Accuracy Metrics

    These metrics are used to find out how well the recommender has placed the items in the final recommended list.

Prediction accuracy

Metrics help us to understand how good the predicted ratings are. Here are some of the prediction accuracy metrics that are used frequently:


denotes the predicted rating for user on item . And is the actual rating. So the closer the value of these metrics to zero, the better the prediction algorithm...