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

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Table of Contents (7 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introducing Machine Learning Predictive Models
Introducing Machine Learning Predictive Models
Characteristics of machine learning predictive models
Types of machine learning predictive models
Working with neural networks
A sample neural network model
Summary
2
Getting Started with Machine Learning
Getting Started with Machine Learning
Demonstrating a neural network
Support Vector Machines
Demonstrating SVMs
Summary
3
Understanding Models
Understanding Models
Models
Using graphs to interpret machine learning models
Using statistics to interpret machine learning models
Using decision trees to interpret machine learning models
Summary
4
Improving Individual Models
Improving Individual Models
Modifying model options
Using a different model to improve results
Removing noise to improve models
Doing additional data preparation
Balancing data
Summary
5
Advanced Ways of Improving Models
Advanced Ways of Improving Models
Combining models
Using propensity scores
Meta-level modeling
Error modeling
Boosting and bagging
Predicting continuous outcomes
Summary
6
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Predicting continuous outcomes

Until now, we have spent all of our time talking about categorical outcomes and most of those examples apply to continuous outcomes, but in this section we're going to focus exclusively on continuous outcome variables.

As I mentioned previously, when we're talking about continuous outcome predictions or variables, everything that we've talked about in this book still applies: the main difference, though, is going to be in terms of how we end up combining predictions.

Here, in this example, we can see that we built three models and we have predictions from each one of those models:

When we want to combine the predictions, all we do is take a mathematical average. The mean of these previous models ends up being the combined prediction because we're not predicting individual categories as we were when we had a categorical outcome...

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