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Many if not most supervised-classification problems involve some degree of class imbalance, where at least one class occurs more frequently than the others. The imbalanced-classification problem illustrates the value of approaching data-science problems as empirical (as well as formal) optimization problems, using techniques termed cost-sensitive learning. This post will show you how to do cost-sensitive binary classification.
Alteryx has a lot of built in functionality, but the ability to leverage custom R code opens up even more possibilities. After reading an answer on the Alteryx Community many months back, I was inspired to try and integrate Google Charts into an Alteryx workflow by using the R tool.
Most real-world data-science design patterns combine several models to solve a single business problem. This post surveys the most common and effective techniques for combining models. Once you make it through this post (and its predecessors), you'll be ready to take on the design patterns we'll begin learning in 2017.
Cross validation (CV) is a difficult topic. There are many ways to do CV, and articles on the subject can be very technical. This blog post is a gentle introduction to CV. Read it and you'll find it much easier to understand later posts describing data-science design patterns that use CV.
Did you want to enchance your already awesome workflow with a cool interactive visualization that you encountered? In this two part series, I am going to show you how, with a little bit of effort, you can achieve your goal.
At the time I'm writing this, we are focused on putting the finishing touches on the 10.6 release (Now available here). Many of the new capabilities that are being introduced with this release are focused on advanced analytics. We are particularly excited about the introduction of four new tools that are focused on prescriptive analytics.