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You walk down one aisle of the grocery store to get your favorite cereal. On the dairy aisle, someone sick from COVID-19 coughs. Did your decision to grab your cereal before your milk possibly keep you healthy? How can these unpredictable, near-random choices be included in complex models?
Extracting additional information about a model trained with a predictive tool is possible because of the way the predictive tools are built - containing an R tool with R code that executes the core functionality of the tool, such as training a model with provided input data.
With the debut of the Python tool in Alteryx Designer 2018.3, many great macros from PDF table extraction to Parquet file integration are born. As a citizen data scientist, the first instinct is to import some great scikit-learn package into the predictive palette. The question is where to start?
Neural Networks are an approach to artificial intelligence that was first proposed in 1944. Modeled loosely on the human brain, Neural Networks consist of a multitude of simple processing nodes (called neurons) that are highly interconnected and send data through these network connections to estimate a target variable. In this article, I will discuss the structure and training of simple neural networks (specifically Multilayer Perceptrons, aka "vanilla neural networks"), as well as demonstrate an example neural network created by the Alteryx Neural Network Tool.
Who doesn’t love a good cheat sheet? Nobody, that’s who. Cheat sheets are awesome. They are a great reference for functions you need handy, but don’t have memorized by heart (yet). They can also be a fantastic way for learning and reinforcing components of a programming language. Some people like to keep them saved as a bookmark on their web browser. With all of that in mind, we are proud to present to you an Alteryx – R Cheat Sheet, which features Alteryx specific functions for use in the R Tool. With this cheat sheet, you should be better equipped to take on any R Tool challenges you encounter.
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.
Have you ever tried to explore those mysterious tools with an R in the lower right-hand corner and been too confused to continue? Or have you not even installed the Predictive, Time Series, and Predictive Grouping tools? Well, here’s your chance to clear through your fog of confusion and utilize these powerful tools.
We recently released a Microsoft Kitthat included a text analytics tool. I wanted to compare the Microsoft sentiment analysis capability to a couple open source algorithms available. Here's how they stack up.
Does this sound familiar? You just watched a fantastic demonstration for advanced users on regression modeling. You think (who wouldn’t?!) “These tools look amazing…imagine what I can do!” So you jump into Alteryx and start plugging in your data! “BUT WAIT! What are all these error messages?!” Don't give up! Read this instead!