Goto

Collaborating Authors

New Book: Credit risk analytics, The R Companion

@machinelearnbot

Credit risk analytics in R will enable you to build credit risk models from start to finish. Accessing real credit data via the accompanying website www.creditriskanalytics.net, you will master a wide range of applications, including building your own PD, LGD and EAD models as well as mastering industry challenges such as reject inference, low default portfolio risk modeling, model validation and stress testing. This book has been written as a companion to Baesens, B., Roesch, D. and Scheule H., Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS, John Wiley & Sons, 2016. Bart Baesens is a professor of Big Data and Analytics at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He has written more than 200 scientific papers and 10 books.


New E-learning course: Profit-driven Business Analytics

@machinelearnbot

The e-learning course on profit-driven business analytics presents a toolbox of advanced analytical approaches that support subsequent cost-optimal decision making. They are advanced in that they are tailored for use in a business setting, where it is crucial to account for the costs and benefits that are related to decision making based on the output of analytical models. We call such approaches profit-driven analytics and they extend and reinforce the abilities of traditional analytics.The profit-driven perspective towards analytics that is advanced in this course contrasts with a traditional statistical perspective, which ignores the costs and benefits related to decision making based on analytical models. In the course, we discuss both profit-driven descriptive and predictive analytics, and as well introduce uplift modeling as a stepping stone toward developing prescriptive analytical models. We also discuss a range of profit-driven evaluation measures for assessing the performance of analytical models from a business perspective.


Book: Analytics in a Big Data World

@machinelearnbot

By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic.


E-learning courses on Advanced Analytics, Credit Risk Modeling, and Fraud Analytics

@machinelearnbot

The E-learning course starts by refreshing the basic concepts of the analytics process model: data preprocessing, analytics and post processing. We then discuss decision trees and ensemble methods (bagging, boosting, random forests), neural networks, support vector machines (SVMs), Bayesian networks, survival analysis, social networks, monitoring and backtesting analytical models. Throughout the course, we extensively refer to our industry and research experience. The E-learning course consists of more than 20 hours of movies, each 5 minutes on average. Quizzes are included to facilitate the understanding of the material.


Do We Need Balanced Sampling?

@machinelearnbot

In many real-world classification tasks such as churn prediction and fraud detection, we often encounter the class imbalance problem, which means one class is significantly outnumbered by the other class. The class imbalance problem brings great challenges to standard classification learning algorithms. Most of them tend to misclassify the minority instances more often than the majority instances on imbalanced data sets. For example, when a model is trained on a data set with 1% of instances from the minority class, a 99% accuracy rate can be achieved simply by classifying all instances as belonging to the majority class. Indeed, the problem of learning on imbalanced data sets is considered to be one of the ten challenging problems in data mining research.