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.

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F# for Machine Learning Essentials
Sudipta Mukherjee
Feb, 2016 | 194 pages
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Table of Contents (16 chapters)
F# for Machine Learning Essentials
F# for Machine Learning Essentials
Credits
Credits
Foreword
Foreword
About the Author
About the Author
Acknowledgments
Acknowledgments
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Introduction to Machine Learning
Introduction to Machine Learning
Objective
Why use F#?
Unsupervised learning
Machine learning frameworks
Machine learning for fun and profit
Recognizing handwritten digits – your "Hello World" ML program
Summary
2
Linear Regression
Linear Regression
Objective
Different types of linear regression algorithms
APIs used
The basics of matrices and vectors (a short and sweet refresher)
QR decomposition of a matrix
Linear regression method of least square
Finding linear regression coefficients using F#
Finding the linear regression coefficients using Math.NET
Putting it together with Math.NET and FsPlot
Multiple linear regression
Multiple linear regression and variations using Math.NET
Weighted linear regression
Plotting the result of multiple linear regression
Ridge regression
Multivariate multiple linear regression
Feature scaling
Summary
3
Classification Techniques
Classification Techniques
Objective
Different classification algorithms you will learn
Some interesting things you can do
Understanding logistic regression
Multiclass classification using logistic regression
Multiclass classification using decision trees
Predicting a traffic jam using a decision tree: a case study
Challenge yourself!
Summary
4
Information Retrieval
Information Retrieval
Objective
Different IR algorithms you will learn
What interesting things can you do?
Information retrieval using tf-idf
5
Collaborative Filtering
Collaborative Filtering
Objective
Different classification algorithms you will learn
Vocabulary of collaborative filtering
Baseline predictors
Item-item collaborative filtering
Top-N recommendations
Evaluating recommendations
Ranking accuracy metrics
Working with real movie review data (Movie Lens)
Summary
6
Sentiment Analysis
Sentiment Analysis
Objective
What you will learn
A baseline algorithm for SA using SentiWordNet lexicons
Handling negations
Identifying praise or criticism with sentiment orientation
Pointwise Mutual Information
Using SO-PMI to find sentiment analysis
Summary
7
Anomaly Detection
Anomaly Detection
Objective
Detecting point anomalies using IQR (Interquartile Range)
Detecting point anomalies using Grubb's test
Grubb's test for multivariate data using Mahalanobis distance
Chi-squared statistic to determine anomalies
Detecting anomalies using density estimation
Strategy to convert a collective anomaly to a point anomaly problem
Dealing with categorical data in collective anomalies
Summary
Index
Index

Weighted linear regression

Sometimes each sample, or in other words, each row of the predictor variable matrix, is treated with different weightage. Normally, weights are given by a diagonal matrix where each element on the diagonal represent the weight for the row. If the weight is represented by W, then theta (or the linear regression coefficient) is given by the following formula:

Math.NET has a special class called WeightedRegression to find theta. If all the elements of the weight matrix are 1 then we have the same linear regression model as before.

The weight matrix is normally determined by taking a look at the new value for which the target variable has to be evaluated. If the new value is depicted as x, then the weights are normally calculated using the following formula:

The numerator of the weight matrix can be calculated as the Euclidean distance between two vectors. The first vector is from the training data and the other is the new vector depicting the new entry for which the...

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