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)

Error modeling

Error modeling is another form of meta-level modeling but in this case we will be modeling cases where there were errors in our predictions. In this way, we can increase the accuracy of that prediction. Using an example, we will walk through how to do error modeling.

Consider the following scenario, for example:

Here, we have a dataset named LoyalTrain. This is just a training dataset; we have our testing and validation dataset at a different place and will build a model only on the training dataset. Theer is also a Type node and a Neural Net model, where we are predicting the variable loyal. Run the Analysis node to see the results as shown in the following screenshot:

You can see that there are two categories in the outcome variable: people are either predicted to stay or to leave. You can also see that correct predictions were made in 79% of the cases. Mistakes...