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

Related Content you might be interested in

Current Title:

Small book image
Machine Learning for Data Mining
Jesus Salcedo
Apr, 2019 | 252 pages
Small book image
IBM SPSS Modeler Essentials
Keith McCormick | Jose Jesus Salcedo
Dec, 2017 | 238 pages
Small book image
Mastering Predictive Analytics with scikit-learn and TensorFlow
Alvaro Fuentes
Sep, 2018 | 154 pages
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
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Modifying model options

Modifying model options to improve the model is one of the straightforward ways to improve a model. We will see how we can do this with the help of an example:

  1. We will create an SVM model just as we did in the second chapter:
  1. Click on Status, go to the Expert tab, and select Expert under Mode. As we have seen in Chapter 2, Getting Started with Machine Learning, whenever we are using SVM models, we need to modify their settings. Change the Regularization parameter to 5. And, in the Kernel type, select Polynomial. Then, click on Run.
  1. You will see a model created. Now, let's do our next step of analyzing the model. Connect your generated model to an Analysis node from the Output palette. Open the Analysis node, and select the Coincidence matrices (for the symbolic targets option) and click on Run. This will be the result that will be acquired:
...
Previous Section
End of Section 2
Next Section