Book Image

Machine Learning for Data Mining

By : Jesus Salcedo
Book Image

Machine Learning for Data Mining

By: Jesus Salcedo

Overview of this book

Machine learning (ML) combined with data mining can give you amazing results in your data mining work by empowering you with several ways to look at data. This book will help you improve your data mining techniques by using smart modeling techniques. This book will teach you how to implement ML algorithms and techniques in your data mining work. It will enable you to pair the best algorithms with the right tools and processes. You will learn how to identify patterns and make predictions with minimal human intervention. You will build different types of ML models, such as the neural network, the Support Vector Machines (SVMs), and the Decision tree. You will see how all of these models works and what kind of data in the dataset they are suited for. You will learn how to combine the results of different models in order to improve accuracy. Topics such as removing noise and handling errors will give you an added edge in model building and optimization. By the end of this book, you will be able to build predictive models and extract information of interest from the dataset
Table of Contents (7 chapters)

Support Vector Machines

Support Vector Machines (SVMs) models were built to predict categorical and continuous outcomes and are especially good when you have many predictors. They were developed for difficult predicting situations where linear models were unable to separate the categories of the outcome field. They too work like black boxes, hiding their complex work in predicting results. Let's get an insight into how SVMs work.

Working with Support Vector Machines

Suppose, for example, there is a kind of data that cannot be separated using a single line as shown in this diagram:

Consider these shapes to be different types of data. As you can see, we won't be able to separate a cluster of data by just drawing a...