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)

Boosting and bagging

The idea behind boosting is that by building successive models that are built to predict the misclassifications of earlier models you're performing a form of error modeling. Bagging, on the other hand, is sampling with replacement. With this method, new training datasets are generated which are of the same size as the original dataset. For our example in this section, will be using a bootstrap sample.

In this example, we're going to see how to do boosting and bagging, which are two methods of improving a model.

Boosting

Let's see how to do boosting with the following steps:

  1. Get your data on a canvas and partition it.
  2. Create a Neural Net model for the data.
  3. Run the Neural Net model with...