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

Understanding logistic regression

Unlike linear regression which is used to predict the real values of a real entity, logistic regression is used to predict the class or tag of an unseen entry. Logistic regression's output is either a 0 or a 1 depicting the predicted class of the unseen entry. Logistic regression uses a smooth curve whose values range from 0 to 1 for all the values of the independent variable.

Sigmoid function (also called logistic function) is one option for this function. This is defined by the following formula:

The sigmoid function chart

The following chart is generated by the code snippet using FsPlot:

You need to install Chrome to get the chart rendered.

So you see that the function value approaches 1 as the value of X approaches infinity, and it approaches 0 as the value of approaches negative infinity. So for any given value of , you can determine the class if you set your threshold at 0.5. In other words, you can say that if for a given value of the value of the sigmoid...