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

Feature scaling

If your features for linear regression are in different ranges, the result produced can be skewed. For example, if one of the features is in the range of 1 to 10 and the other is in the range of 3,000 to 50,000, then the predicted model will be bad. In such cases, the features must be rescaled so that they belong to almost the same range—ideally between 0 and 1.

The common strategy to scale a feature is to find the average and the range, and then using the following formula, all the values are updated:

In the preceding formula, is the mean or average of the values of feature and is the range or the standard deviation. The following code snippet shows how you can perform a feature scaling using F#.

You can calculate the feature scaling features by hand like this and then update the predictor matrix manually.

After the scaling, the house details matrix looks like this:

These values of the scaled matrix are very close to each other and thus the linear model generated.