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Ever wondered how to build a new analytic tool from scratch using the Alteryx Python SDK, but didn’t know where to start? This blog post takes you through the absolute basics to get you up and running - You’ll be creating brand new tools, connectors and advanced analytics in no time with this step-by-step beginners guide!
Voronoi Tesselation and Delaunay Trianglulation both perform spatial calculations on a set of irregular points. Voronoi Cells (sometimes referred to as Thiessen Polygons in the GIS world) make up a Voronoi Tesselation, which is the partitioning of a plane into polygons based on a set of points, so that for each point there is a corresponding polygon where the area in the polygon is closer to the corresponding point than any other point. Delaunay Triangulation is when a set of irregular points are divided into triangles, so that no point in the set is inside the circumcircle of any triangle created from the points.
Both of these processes have a bunch of really neat spatial analysis applications. In this article, we will talk about their implementation in Alteryx.
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.