Competitive Model Stacking: An Introduction to Stacknet Meta Modelling Framework

Abstract: StackNet is a computational, scalable and analytical framework mainly implemented in Java that resembles a feedforward neural network and uses Wolpert’s stacked generalization in multiple levels to improve the accuracy of predictions. StackNet will be demonstrated through practical examples.

Bio: Marios Michailidis is a Research data scientist at H2O.ai . He holds a Bsc in accounting Finance from the University of Macedonia in Greece and an Msc in Risk Management from the University of Southampton. He has also nearly finished his PhD in machine learning at University College London (UCL) with a focus on ensemble modelling. He has worked in both marketing and credit sectors in the UK Market and has led many analytics’ projects with various themes including: Acquisition, Retention, Recommenders, Uplift, fraud detection, portfolio optimization and more.

He is the creator of KazAnova(http://www.kazanovaforanalytics.com/), a freeware GUI for credit scoring and data mining 100% made in Java as well as is the creator of StackNet Meta-Modelling Framework (https://github.com/kaz-Anova/StackNet). In his spare time he loves competing on data science challenges and was ranked 1st out of 500,000 members in the popular Kaggle.com data competition platform. Here (http://blog.kaggle.com/2016/02/10/profiling-top-kagglers-kazanova-new-1-in-the-world/) is a blog about Marios being ranked at the top in Kaggle and sharing his knowledge with tricks and ideas.

Open Data Science Conference